Effects of pollution in the Hudson River on commerce
Bibliographic record
Abstract
From the time of Henry Hudson’s exploration of the Hudson in the early 17th century, if not before, the Hudson River has been a powerful force for commerce on the East Coast of America. Its fortune is inexorably linked to that of New York City, and all of New York State. How did the Hudson move from being a tool of commerce and industry into a force that drove against big business’s use and exploitation of it? This paper focuses on the twentieth century conflicts between commercial and individual and environmental use of New York’s Hudson river. Commercial use of the Hudson from companies like General Electric and Consolidated Edison started to become an environmental problem in the second quarter of the twentieth century1, though awareness only really began in the 1960s, with groups like Scenic Hudson starting to organize responses in the 1970s. Reactions in the public view and the media increased dramatically from 1970s through the 1990s, and has continued into the twenty-first century. How did what was once a convenient resource to be exploited transform into a billions of dollars of costs to corporate America? It’s stunning to look at how most of the damage (and recovery!) of the Hudson River watershed has happened within the the last century. It is only in the last century that mankind has gained to ability to so seriously damage the environment, and far less recently 1 G.E. received a permit for discharge of PCBs into the Hudson as early as 1930. This suggests that the dumping may have begun substantially earlier... [Lewis], p. 271.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".