Programa del Medio Ambiente de las Naciones Unidas (UNEP), Preguntas y Respuestas acerca de los Efectos de la Disminución del Ozono, la Radiación Ultravioleta y el Clima en los humanos y el Medio Ambiente. Suplemento del Reporte de Evaluación 2022 del Panel de Evaluación de los Efectos en el Medio Ambiente de las Naciones Unidas
Bibliographic record
Abstract
Esta colección de preguntas y respuestas fue preparada por el Panel de Evaluación de los Efectos en el Medio Ambiente (EEAP por sus siglas en inglés) del Protocolo de Montreal bajo el amparo del Programa para el Medio Ambiente de las Naciones Unidas (PNUM). Este documento es un complemento de la Evaluación Cuatrienal 2022 realizada por el EEAP (https://ozone. unep.org/science/assessment/eeap), y provee de información interesante y útil para redactores de políticas públicas, maestros y científicos. Este P&Rs está escrito en un lenguaje de fácil comprensión para el público en general.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".