Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Lockdowns designed to slow the spread of COVID-19 caused a large decrease in vehicle traffic in 2020. As a result, global carbon dioxide emissions plummeted. Scientists wondered whether these short-term changes would be significant enough to affect the climate. Now, a team led by John Fyfe of Environment and Climate Change Canada has an early look at the answer. The results show that the emission reductions caused by the pandemic led to undetectably small effects on global average temperatures ( Sci. Adv. 2021, DOI: 10.1126/sciadv.abf7133 ). In early 2020, as it became clear that the pandemic would cause large-scale emission changes, Fyfe and his colleagues used a powerful climate model to test a few projections for what might happen over 2 years of reduced emissions. They modeled scenarios in which CO 2 emissions and sulfate aerosol levels dropped as low as 25, 50, or 100% during that period, then looked
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.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 it