Bibliographic record
Abstract
Most U.S. citizens would respond positively to the question “Are you concerned about having a healthy environment in which to live?” The proportion of positive responses grows smaller as the questions shift to “Are you responsible for the quality of the environment in which you live? OR “Are you willing to change your behaviors in order to achieve a healthy environment?” The realization that many of our actions affect not only our immediate environment but also environments far away from the source of the offending pollutant is not a commonly held understanding. In many ways, we ALL live downstream from the polluting nature of our fellow world co-inhabitants and upstream of our inputs.Acid rain falling on the northeastern United States and Canadian lakes and forests has its origin hundreds of miles upstream (as the crow flies) at the coal-and fossil-fuel burning plants of the Ohio River Valley. As much as 10 to 40 percent of the loading of nitrogen to estuarine and coastal waters around the world comes from atmospheric deposition—Baltic Sea, Kiel Bight, North Sea, Long Island Sound, New York Bight, Chesapeake Bay, Pamlico
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.009 |
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".