Bibliographic record
Abstract
Climate change continues to intensify. Last month, record-breaking high temperatures —above 40 °C—scorched the US Pacific Northwest and southwestern Canada. North America marked its hottest June, and Europe sweltered through its second-warmest June on record, according to the European Union–supported Copernicus Climate Change Service . As temperatures rise, talks sponsored by the United Nations aimed at curtailing this environmental crisis are restarting after pausing for the COVID-19 pandemic. Negotiators are now meeting virtually, resuming where their talks ended 18 months ago at a previous global climate conference, in Madrid. Their goal is to finish a set of rules for countries to follow as they fulfill pledges made under the 2015 Paris Agreement to control greenhouse gas emissions. Reducing these emissions will require innovations from the chemical sciences in materials, energy efficiency, batteries, renewable energy, and technologies to capture and sequester carbon dioxide . Negotiators hope to wrap up details in
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".