Supplementary material to "Time varying changes and uncertainties in the CMIP6 ocean carbon sink from global to regional to local scale"
Bibliographic record
Abstract
The data used in this study was accessed through the PANGEO CMIP6 catalogue on Google Cloud (https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv), and cross verified against data published on the Earth System Grid Federation (ESGF, https://esgf-node.llnl.gov/search/cmip6/).Sixteen models submitted at least one realization for historical, ssp126, ssp245, and ssp585 scenarios (Table S1).Three models were excluded, leaving 13 total models in our analysis.Among these, NorESM2-MM was excluded because the same realization was not available for all three scenarios.BCC-CSM2-MR was excluded because it showed sink values that were three orders of magnitude larger than other models.To make sure this is not an issue with the uploaded dataset to google cloud, the historical dataset was downloaded directly from ESGF and yielded the same results.The units according to the published metadata are kg m -2 s -1 of carbon, but may be in error.CNRM-ESM2-1 was excluded because the historical model results are out of the range of uncertainty of the observation data over 1960-2020 from the Global Carbon Project of 2021 (Friedlingstein et al., 2021) by a large offset (lower pink line in Fig. S1). Institution Model(s)Main Reference(s) Realization(s)
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.684 | 0.204 |
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".