LETTER FROM HEADQUARTERS, POLICY PROGRAM NOTES, ABOUT OUR MEMBERS, LIVING ON THE REAL WORLD, AMS STATEMENT, AMS STATEMENT, STUDENTS
Bibliographic record
Abstract
cientific evidence relating to the climate system and the impact that people might be having on it spans dozens of fields of study and includes work from tens of thousands of individual scientists.The evidence comes from decades of intensive research and is based on observations, field and laboratory experiments, and model simulations.Over the past few decades, there have been hundreds of independent scientific assessments of this body of evidence.These assessments synthesize scientific research to determine what is known and with what level of confidence.Assessments have examined virtually every aspect of the climate issue, including how the climate system works, what is happening to it and why (the role of natural and human influences), what may happen in the future, what the consequences could be for natural and human systems, and what could be done to manage the risks.Many assessments are scientifically rigorous, produced using transparent processes, and include evaluation of uncertainty and confidence.This Policy Program Note considers best practices in the assessment process, compares best practices with recent high-profile climate assessments, and identifies overarching scientific conclusions. EFFECTIVE ASSESSMENT PRACTICES.Assessments of science are most effective when they are relevant to user needs, credible (scientifically rigorous and accurate), and legitimate (produced in a transparent and fair process).Credibility and legitimacy (this memo's focus) are enhanced when assessments
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.023 | 0.014 |
| Insufficient payload (model declined to judge) | 0.112 | 0.068 |
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".