Connect with us


Building the dams that doomed a valley



Swift River map

As an MIT senior, Jerome “Jerre” Spurr had paid little attention to the articles in the Boston Globe about the new reservoir planned for Western Massachusetts. But in 1927, just a month before his graduation, he found himself in a face-to-face interview with Frank Winsor, the chief engineer of the massive construction project.

Winsor had personally visited MIT to recruit top engineering graduates to help build the new reservoir, which—at 18 miles long and up to six miles wide—would be the largest in the world devoted solely to drinking water. Spurr, who grew up in Dorchester, had completed a bachelor’s degree in civil engineering with a focus on soil sciences and had been mentored by the soil sciences pioneer Karl Terzaghi, but he hadn’t thought much about what he’d do next. Suddenly Winsor had chosen him to lead a contingent of other MIT graduates to the Swift River Valley, the site of the future reservoir, immediately after graduation.

Spurr and at least six other new MIT grads set out for Enfield, Massachusetts, the largest of four small towns on the floor of the valley, 65 miles west of Cambridge. They may have understood the significance of their arrival intellectually, but they didn’t grasp it viscerally: they would play a part in destroying everything in the valley. Every building would be razed, every grave dug up, every tree cut, every farm stripped to a moonlike subsoil—every organic item in that green basin removed so that metropolitan Boston would be provided with fresh, clean drinking water in perpetuity. The four towns of the Swift River Valley—Enfield, Dana, Greenwich, and Prescott—would be wiped off maps as if they had never existed, replaced by 412 billion gallons of water. In all, nearly 2,500 people from the four doomed towns and sections of those around them would be displaced. 

The locals were, understandably, angry and suspicious of these college men in their natty outfits and fast cars. Their own young men had left the valley in search of better work. 

Of course, the MIT engineers, who were soon joined by a cohort of Northeastern and Worcester Polytechnic graduates, were not the first interlopers to descend upon the Swift River Valley, nor were the residents of Enfield, Dana, Greenwich, and Prescott the first people forced to leave it. Native Americans who had once lived among its many lakes, ponds, and streams had called it Quabbin, meaning “the meeting of many waters” or “a well-watered place.” The name Quabbin Reservoir, officially adopted in 1932, was a nod to the countless generations who had first populated the valley.

The massive effort to construct the reservoir was made up of several overlapping engineering projects: digging the 24.6-mile-long Quabbin Aqueduct between the new reservoir and the Wachusett Reservoir northeast of Worcester (the second-longest tunnel in the world at the time); orchestrating the water’s flow to Boston through a massive 80-mile network of rock tunnels and concrete pipes; building the Winsor Dam, the Goodnough Dike, and the “baffle dam” in the center of the reservoir (to purify sediment-filled water from the Ware River by circulating it); digging the “diversion tunnel,” which rerouted the Swift River; disinterring more than 7,600 bodies from their graves and reinterring 6,601 of them in the new Quabbin Park Cemetery (others went elsewhere at families’ requests); constructing the Quabbin Administration Building; building the Daniel Shays Highway (Route 202) around the western edge of the reservoir; and reforesting the watershed, at Quabbin Park and throughout the vast Quabbin Reservation.

A map shows how the reservoir would change the Swift River Valley.


But first, there was much surveying to be done. Every piece of property in the valley, and every acre of woodland and water, had to be documented and in most cases photographed. The new college graduates were assigned to surveying teams and set off across the valley with their equipment, often using axes to hack their way through brush. Spurr worked at first as a “rodman” and then as an “instrument man,” the top-ranking assistant on a surveying team, helping the foreman complete surveys and blueprints.

Spurr enjoyed the difficult outdoor work, but he was saddened by the townspeople’s initial antipathy. In Enfield, where most of the engineers lived, many refused to take in the young men as boarders even though they desperately needed the income. After bouncing between several homes, including one where the engineers and the landlords sat down to dinner together every night, Spurr decided he’d prefer to live alone and rented a farmhouse from the Metropolitan District Water Supply Commission, which had recently purchased it from one of many valley residents leaving for towns just outside the proposed watershed. During the day, Spurr and the other engineers worked out of the Chandler House, an elaborate white Victorian mansion the size of a small hotel. And knowing he’d be in the valley for at least eight more years, he began renovating the farmhouse in his off hours.

Spurr was soon promoted from instrument man to deed analyst. The commission’s head of the Enfield office, N. LeRoy Hammond, was impressed with his “analytical mind” and promoted him again, making him the head of the Enfield Soil Division, with a staff of five working under him.

As construction of the Winsor Dam ramped up, the engineers had to test the local soil daily. The massive hydraulic fill dam was built atop a row of sunken concrete caissons attached to bedrock, and its approximately 40-foot-wide core was constructed from repurposed earth and rocks from the valley. Swift River water was piped to the top of the construction site, and after the water ran down the core, the fill was expected to solidify into a material completely impervious to the millions of gallons of water beating against it.  

The engineers at the dam had built an artificial lake at its western base, complete with pontoon boats, to monitor the construction. With each new layer of fill, Spurr said in a 1987 interview, there was the potential for “spits” of sand from the “beach”—the strip of land between the artificial lake and the dam—to penetrate the core, making it permeable. And if bits of the core material extended into the beach, they could create planes of weakness that might eventually slide. The fill had no stability in itself: according to Spurr, it had the consistency of molasses. Each day, he and his soil “sample party” tested the dam’s core and the soil on its beach, collecting 15-pound bags and driving them back to the lab next to the Chandler House to analyze their density and composition. 

They also analyzed samples from the core itself. Spurr had helped Terzaghi develop what he described as “a core sampling device that consisted of a tube, and in the tube was a piston, and the piston was connected through a smaller tube and a rod to the end of the extension, and the sampler could be pushed down into the core to the desired depth of the pool.” The piston would automatically suck up the sample in the core section. Then the sample party filled pint jars with the tubes’ contents, marking them with the location and depth. As the dam grew, contractors built “observation wells” so Spurr’s team could climb down and take samples from inside the dam. Fast-paced analysis under high-stakes conditions was “challenging work,” Spurr said, “because it was new and we developed [the technology and procedures] ourselves.” Terzaghi, the founder of modern soil mechanics, had invented the tools; Spurr was deploying them for the first time, flagging problems with the samples so the head dam engineer could take corrective actions to ensure that the structure would be solid enough to contain the water. 

“It was necessary to keep as current a testing operation as possible,” Spurr said. A pervious dam, he added, would be like millions of gallons of molasses—a frightening image for someone who would have remembered the Great Molasses Flood of 1919, a Boston disaster that killed 21 people.

The economy compounded the project’s devastating impact on the valley’s inhabitants. Most sold their properties to the commission at Depression prices, leaving them with little to show for what often amounted to generations of work. No longer able to farm, many had to take whatever jobs they could get—often with the very commission that was forcing them out of their homes.   

Spurr (second row, left) helped lead the Grand March at the farewell ball in 1938.


Although work consumed most of Spurr’s days, including Saturdays, he felt a responsibility to give back to the people whose lives and livelihoods he was irrevocably altering. So he tried to make himself useful in the community. He taught Sunday school. He started a Boy Scout troop. He met his wife, Anna Chase, a Vermont native who had come to Enfield to teach, when she sang in the church choir. Anna herself became the last president of the local women’s social society, the Quabbin Club, and, according to Spurr, played piano for free at the funerals of those who could otherwise not afford music. He joined the Enfield Masons, serving as the chapter’s “Worshipful Master” and then its treasurer; as of the mid-1930s, the group’s roster included as many engineers as locals. By then, Spurr and many other engineers had become part of the community. They formed jazz bands, played on baseball teams, gave out school awards, and married local girls. As Christmas approached in 1934, a year when few families could afford decorations, several of them pitched in to buy electric Christmas lights and strung them on a large tree they erected in the cellar hole of a torn-down commercial building. They also convinced the electric company to turn the site’s power back on until after New Year’s Day. It became the town tree and could be seen from all over the valley.

Nearly 11 years after Spurr’s arrival in Enfield, the dam construction was entering its final phase. Despite meddling and graft at the highest levels of state government, the Quabbin project would be completed ahead of schedule and under budget. With the four towns of the Swift River Valley set to be disincorporated at midnight on April 28, 1938, the Enfield Volunteer Fire Department sponsored a farewell ball. On the night of April 27, thousands of guests in formalwear or black mourning, flanked by journalists with flash cameras and notepads, arrived at the Enfield Town Hall, an old brick building designed to hold a maximum of 300 people. As befit his place in the community, Spurr was near the head of the line for the ball’s Grand March, and he presumably shed tears along with everyone else when the clock struck midnight and the band played “Auld Lang Syne.”

The Swift River Valley was slated for flooding in 1939; by mid-1938 the area was stripped of growing things and empty of stores, schools, and churches. Spurr had hoped to purchase his antique Enfield home from the commission and move it to another location before the waters rose, but a hurricane in September 1938 knocked out his telephone, electricity, and plumbing, none of which would be reinstated. So he and his wife and their young son moved to Wellesley, and he began working on the next stage of the Quabbin project: pressure tunnels carrying water from the Wachusett Reservoir to the Norumbega Reservoir in Weston. He was the last engineer to leave the valley. 

With World War II looming, Spurr left the commission in early 1941 to become an assistant professor of military science and tactics at MIT and head of the MIT ROTC Engineering Unit; in his spare time, he lectured on the Quabbin Reservoir, using the movies he and other engineers had shot on rare and expensive Technicolor film during construction. He went on to serve in Austria and Poland in the Army Corps of Engineers, fought in the Korean War, and later attempted to launch a Boy Scout organization in Turkey while supporting US military missions there. After retiring from the military in 1958, he occasionally lectured on Quabbin construction history and taught a class in soil sciences at Wentworth. When the farewell ball was re-created in Amherst in 1988 to mark the 50th anniversary of the death of the Swift River Valley, Spurr, 83, once again stood at the head of the Grand March. 

At a lecture in the 1980s, a former valley native who had become an engineer after being mentored by Spurr introduced him as “a pioneer … one of the six or eight people in the world that were in on the ground floor [of modern soil engineering].” Spurr walked onto the auditorium stage to applause. “I will simply introduce my remarks by saying that all you have listened to is very much inflated,” he said in his Boston gentleman’s accent. “I am very thankful for the kind comments that have been made, and I don’t know if I can live up to them or not, but I will try.” 

And then the old MIT engineer launched into a technically complex 90-minute lecture on engineering work that he had finished half a century earlier and still knew by heart.

Elisabeth C. Rosenberg is the author of Before the Flood: Destruction, Community, and Survival in the Drowned Towns of the Quabbin, due out from Pegasus Books in August.


ChatGPT is about to revolutionize the economy. We need to decide what that looks like.



ChatGPT is about to revolutionize the economy.  We need to decide what that looks like.

Power struggle

When Anton Korinek, an economist at the University of Virginia and a fellow at the Brookings Institution, got access to the new generation of large language models such as ChatGPT, he did what a lot of us did: he began playing around with them to see how they might help his work. He carefully documented their performance in a paper in February, noting how well they handled 25 “use cases,” from brainstorming and editing text (very useful) to coding (pretty good with some help) to doing math (not great).

ChatGPT did explain one of the most fundamental principles in economics incorrectly, says Korinek: “It screwed up really badly.” But the mistake, easily spotted, was quickly forgiven in light of the benefits. “I can tell you that it makes me, as a cognitive worker, more productive,” he says. “Hands down, no question for me that I’m more productive when I use a language model.” 

When GPT-4 came out, he tested its performance on the same 25 questions that he documented in February, and it performed far better. There were fewer instances of making stuff up; it also did much better on the math assignments, says Korinek.

Since ChatGPT and other AI bots automate cognitive work, as opposed to physical tasks that require investments in equipment and infrastructure, a boost to economic productivity could happen far more quickly than in past technological revolutions, says Korinek. “I think we may see a greater boost to productivity by the end of the year—certainly by 2024,” he says. 

Who will control the future of this amazing technology?

What’s more, he says, in the longer term, the way the AI models can make researchers like himself more productive has the potential to drive technological progress. 

That potential of large language models is already turning up in research in the physical sciences. Berend Smit, who runs a chemical engineering lab at EPFL in Lausanne, Switzerland, is an expert on using machine learning to discover new materials. Last year, after one of his graduate students, Kevin Maik Jablonka, showed some interesting results using GPT-3, Smit asked him to demonstrate that GPT-3 is, in fact, useless for the kinds of sophisticated machine-learning studies his group does to predict the properties of compounds.

“He failed completely,” jokes Smit.

It turns out that after being fine-tuned for a few minutes with a few relevant examples, the model performs as well as advanced machine-learning tools specially developed for chemistry in answering basic questions about things like the solubility of a compound or its reactivity. Simply give it the name of a compound, and it can predict various properties based on the structure.

Continue Reading


Newly revealed coronavirus data has reignited a debate over the virus’s origins



Newly revealed coronavirus data has reignited a debate over the virus’s origins

Data collected in 2020—and kept from public view since then—potentially adds weight to the animal theory. It highlights a potential suspect: the raccoon dog. But exactly how much weight it adds depends on who you ask. New analyses of the data have only reignited the debate, and stirred up some serious drama.

The current ruckus starts with a study shared by Chinese scientists back in February 2022. In a preprint (a scientific paper that has not yet been peer-reviewed or published in a journal), George Gao of the Chinese Center for Disease Control and Prevention (CCDC) and his colleagues described how they collected and analyzed 1,380 samples from the Huanan Seafood Market.

These samples were collected between January and March 2020, just after the market was closed. At the time, the team wrote that they only found coronavirus in samples alongside genetic material from people.

There were a lot of animals on sale at this market, which sold more than just seafood. The Gao paper features a long list, including chickens, ducks, geese, pheasants, doves, deer, badgers, rabbits, bamboo rats, porcupines, hedgehogs, crocodiles, snakes, and salamanders. And that list is not exhaustive—there are reports of other animals being traded there, including raccoon dogs. We’ll come back to them later.

But Gao and his colleagues reported that they didn’t find the coronavirus in any of the 18 species of animal they looked at. They suggested that it was humans who most likely brought the virus to the market, which ended up being the first known epicenter of the outbreak.

Fast-forward to March 2023. On March 4, Florence Débarre, an evolutionary biologist at Sorbonne University in Paris, spotted some data that had been uploaded to GISAID, a website that allows researchers to share genetic data to help them study and track viruses that cause infectious diseases. The data appeared to have been uploaded in June 2022. It seemed to have been collected by Gao and his colleagues for their February 2022 study, although it had not been included in the actual paper.

Continue Reading


Fostering innovation through a culture of curiosity



Fostering innovation through a culture of curiosity

And so I think a big part of it as a company, by setting these ambitious goals, it forces us to say if we want to be number one, if we want to be top tier in these areas, if we want to continue to generate results, how do we get there using technology? And so that really forces us to throw away our assumptions because you can’t follow somebody, if you want to be number one you can’t follow someone to become number one. And so we understand that the path to get there, it’s through, of course, technology and the software and the enablement and the investment, but it really is by becoming goal-oriented. And if we look at these examples of how do we create the infrastructure on the technology side to support these ambitious goals, we ourselves have to be ambitious in turn because if we bring a solution that’s also a me too, that’s a copycat, that doesn’t have differentiation, that’s not going to propel us, for example, to be a top 10 supply chain. It just doesn’t pass muster.

So I think at the top level, it starts with the business ambition. And then from there we can organize ourselves at the intersection of the business ambition and the technology trends to have those very rich discussions and being the glue of how do we put together so many moving pieces because we’re constantly scanning the technology landscape for new advancing and emerging technologies that can come in and be a part of achieving that mission. And so that’s how we set it up on the process side. As an example, I think one of the things, and it’s also innovation, but it doesn’t get talked about as much, but for the community out there, I think it’s going to be very relevant is, how do we stay on top of the data sovereignty questions and data localization? There’s a lot of work that needs to go into rethinking what your cloud, private, public, edge, on-premise look like going forward so that we can remain cutting edge and competitive in each of our markets while meeting the increasing guidance that we’re getting from countries and regulatory agencies about data localization and data sovereignty.

And so in our case, as a global company that’s listed in Hong Kong and we operate all around the world, we’ve had to really think deeply about the architecture of our solutions and apply innovation in how we can architect for a longer term growth, but in a world that’s increasingly uncertain. So I think there’s a lot of drivers in some sense, which is our corporate aspirations, our operating environment, which has continued to have a lot of uncertainty, and that really forces us to take a very sharp lens on what cutting edge looks like. And it’s not always the bright and shiny technology. Cutting edge could mean going to the executive committee and saying, Hey, we’re going to face a challenge about compliance. Here’s the innovation we’re bringing about architecture so that we can handle not just the next country or regulatory regime that we have to comply with, but the next 10, the next 50.

Laurel: Well, and to follow up with a bit more of a specific example, how does R&D help improve manufacturing in the software supply chain as well as emerging technologies like artificial intelligence and the industrial metaverse?

Art: Oh, I love this one because this is the perfect example of there’s a lot happening in the technology industry and there’s so much back to the earlier point of applied curiosity and how we can try this. So specifically around artificial intelligence and industrial metaverse, I think those go really well together with what are Lenovo’s natural strengths. Our heritage is as a leading global manufacturer, and now we’re looking to also transition to services-led, but applying AI and technologies like the metaverse to our factories. I think it’s almost easier to talk about the inverse, Laurel, which is if we… Because, and I remember very clearly we’ve mapped this out, there’s no area within the supply chain and manufacturing that is not touched by these areas. If I think about an example, actually, it’s very timely that we’re having this discussion. Lenovo was recognized just a few weeks ago at the World Economic Forum as part of the global lighthouse network on leading manufacturing.

And that’s based very much on applying around AI and metaverse technologies and embedding them into every aspect of what we do about our own supply chain and manufacturing network. And so if I pick a couple of examples on the quality side within the factory, we’ve implemented a combination of digital twin technology around how we can design to cost, design to quality in ways that are much faster than before, where we can prototype in the digital world where it’s faster and lower cost and correcting errors is more upfront and timely. So we are able to much more quickly iterate on our products. We’re able to have better quality. We’ve taken advanced computer vision so that we’re able to identify quality defects earlier on. We’re able to implement technologies around the industrial metaverse so that we can train our factory workers more effectively and better using aspects of AR and VR.

And we’re also able to, one of the really important parts of running an effective manufacturing operation is actually production planning, because there’s so many thousands of parts that are coming in, and I think everyone who’s listening knows how much uncertainty and volatility there have been in supply chains. So how do you take such a multi-thousand dimensional planning problem and optimize that? Those are things where we apply smart production planning models to keep our factories fully running so that we can meet our customer delivery dates. So I don’t want to drone on, but I think literally the answer was: there is no place, if you think about logistics, planning, production, scheduling, shipping, where we didn’t find AI and metaverse use cases that were able to significantly enhance the way we run our operations. And again, we’re doing this internally and that’s why we’re very proud that the World Economic Forum recognized us as a global lighthouse network manufacturing member.

Laurel: It’s certainly important, especially when we’re bringing together computing and IT environments in this increasing complexity. So as businesses continue to transform and accelerate their transformations, how do you build resiliency throughout Lenovo? Because that is certainly another foundational characteristic that is so necessary.

Continue Reading

Copyright © 2021 Seminole Press.