GRADE guidelines for environmental and occupational health: A new series of articles in Environment International
Bibliographic record
Abstract
In 2014, early in the GRADE Working Group's initiative of expanding evidence assessment and guideline develop to address specific topic areas, the working group created the Environmental and Occupational Health Project Group. The project group began by developing a research agenda to advance the rigor and transparency of systematic reviews and guideline development in this field by adapting GRADE to support decision-making in environmental health. In a commentary for Environment International, we first introduced GRADE, examined steps of the guideline development process currently used for decision-making, and outlined suggestions for a research agenda to address decision-making in the environmental and occupational health field (Morgan et al., 2016).
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 | 0.062 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.052 | 0.048 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".