Bibliographic record
Abstract
need a cohesive framework to evaluate the effectiveness Because advisories have been posted for so many water bodies against of regulations on mercury consumption of fish with elevated emissions in the United concentrations of potentially dan-States and Canada. gerous methylmercury (MeHg), regulations limiting mercury emissions have been promulgated in many countries or are likely to be put forward in the near future (1–5). Yet, many questions about the environmental benefits of emissions reductions remain unanswered. Current computer models and other assessment tools provide widely divergent estimates for the effectiveness of emissions controls at reducing MeHg levels in fish (6–8). In addition, no broad-scale data sets are available to test model predictions. Some intensive studies and syntheses of regional databases have been conducted, but their overall applicability to different ecosystems or at the continental scale is uncertain. The problem is that the terrestrial–aquatic mercury cycle is complex, with many nonlinear processes that link atmospheric mercury emissions and MeHg bioaccumulation in fish and wildlife (7; Figure 1). As a result, how effective emissions reductions will be in de-
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.477 | 0.366 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".