Silent Spring revisited: Trends in Environmental Engineering and Sciences, from the 1960s to Today
Bibliographic record
Abstract
While Silent Spring was responsible for bringing the massive environmental issues of the post-war world to the attention of the voting public, scientists and engineers had been concerned for decades about pollution from radioactive fall-out, indiscriminate spraying of pesticides, and unsafe drinking water. This concern led to the formation of the most powerful environmental regulatory agency in the world, the United States Environmental Protection Agency (USEPA), an organization whose scientists and engineers and sociologists and economists have been tasked to figure out policies to save "Spaceship Earth". Today, the formidable knowledge and power represented by the USEPA are under threat of being destroyed and the very strategies that have helped to make the North American environment the envy of the world are in grave danger of being dismantled. In understanding the historical underpinnings, we can attempt to move forward with sustainable solutions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".