A4D standardized diagnostics software and technical reports
Bibliographic record
Abstract
This zenodo repo contains version 1.0 of the CCCma/A4D standard diagnostic package, which is a package that compares model simulations with observations. It is built on top of ESMValtool, using an in-house python routine. Code and documentation are in zenodo_A4D_software.tar. In addition, this repo contains reports of the CanESM5.0-p2, CanESM5.1-p1 and CanESM5.1-p2 climates and the comparison to observations, which were produced with version 1.0 of the CCCma/A4D standard diagnostic package (details see below). These reports will be updated with new diagnostics and future model versions, and made available at https://gitlab.com/cccma/canesm/-/wikis/home . The data used to generate the plots used in Sigmond et al. (2023) is not part of this repository. It is available from the ESGF as described in the ``Code and data availability'' of the paper. REPORTS IN THIS REPO: Comparisons of the historical simulations with selected observations and analyses: v1.0_CanESM5.0_p2_hist_vs_obs.pdf v1.0_CanESM5.1_p1_hist_vs_obs.pdf v1.0_CanESM5.1_p2_hist_vs_obs.pdf Comparisons of preindustrial control simulations: v1.0_CanESM5.1-p1_vs_CanESM5.0-p2_piControl.pdf v1.0_CanESM5.1-p2_vs_CanESM5.1-p1_piControl.pdf Equilibrium Climate Sensitivity: v1.0_CanESM5.0_p2_ECS.pdf v1.0_CanESM5.1_p1_ECS.pdf v1.0_CanESM5.1_p2_ECS.pdf
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.326 | 0.189 |
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".