CanESM5 data for CCCma COVID-19 climate scenarios
Bibliographic record
Abstract
This data is associated with the publication:<br> <br> <strong> Quantifying the Influence of COVID-19 Emission Reductions on Climate</strong> <br> John C. Fyfe, Viatcheslav V. Kharin, Neil Swart, Gregory M. Flato, Michael<br> Sigmond and Nathan Gillett<br> <br> Canadian Centre for Climate Modelling and Analysis, Environment and Climate<br> Change Canada, Victoria, British Columbia, V8W 2Y2, Canada.<br> <br> The data includes the monthly CO2 emissions used to drive CanESM5, and also the<br> monthly CO2 concentrations, and Global Mean Screen Temperatures resulting from<br> the model simulations. Using this data, Figure 1 of the paper can be completely<br> reproduced. The organisation of the data is described in the readme.txt file. All contents are collected into a tar archive.<br> <br>
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 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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.019 |
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; both teacher heads agree on what is shown here.
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".