Bibliographic record
Abstract
T he Hudson River pours out from Lake Tear of the Clouds, in New York's high peaks of the Adirondack Mountains.The river travels 315 miles through mountains and cliffs, farmlands and industrial parks, towns and cities.Throughout its course, the Hudson feeds fertile lands and thirsty cities.Its beauty inspired the first school of painting in the nation, its waters teem with fishand its contamination distinguishes it as the longest Superfund site in the United States.Superfund sites are those identified by the U.S. Environmental Protection Agency (EPA) as most seriously contaminated with hazardous waste.The Superfund Basic Research Program (SBRP) is a joint program of the EPA and the NIEHS.The SBRP currently funds multidisciplinary research in 19 university centers that focus on acquiring new scientific knowledge to advance understanding of human and ecological risks from hazardous substances, and to develop new environmental technologies for cleaning up Superfund sites.SBRPs at a number of institutions, including Mount Sinai School of Medicine in New York City and New York University's Nelson Institute of Environmental Medicine in Tuxedo, are contributing to this body of knowledge through a number of health effects research studies, as well as through an outreach program to train the next generation of environmental scientists, using the Hudson River cleanup as an educational nexus.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".