Bibliographic record
Abstract
This report is dedicated to the memory of our friend and colleague, Prof. Gérard Mégie of the Centre National de la Recherche Scientifique of Paris, France. Gérard worked as a member of the Scientific Assessment Panel of the Montreal Protocol since the very beginning; he was a participant in the Les Diablerets Panel Review Meeting in 1989. Later, he became a Cochair of the Scientific Assessment Panel, cochairing the 1998 and 2002 assessment reports. To that role, he brought his rare combination of scientific excellence and leadership talent, built upon many years as a teacher, researcher, internationally acclaimed scientist, and leader in the atmospheric sci-ence community. One mark of Gérard’s insightfulness was his work to guide the Panel toward its increasing emphasis on the connections between the ozone layer and the climate system. The Panel’s assessment report for 2006 does contain that emphasis, and indeed it is captured in separate chapters on these issues. Gérard was dedicated to communicating scientific understanding at many levels, ranging from international decisionmaking to the realm of the general public and schools. This is exemplified by his thoughtful work associated with the Panel’s efforts to provide a set of “frequently asked ” questions and answers about the ozone layer.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.118 | 0.084 |
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".