CMIP drop in session reports: Variables
Bibliographic record
Abstract
A series of regular drop-in sessions was launched in early 2023, facilitated by the CMIP International Project Office (CMIP IPO), to cover key aspects of CMIP7 design and development and promote community feedback to the CMIP governance and Task Teams. This report provides a summary of the first CMIP Variables drop-in sessions held on 1 June 2023 and 8 June 2023 across two timeslots (16:00 UTC and 05:00 UTC respectively) to support equitable global participation. Each session was chaired by either CMIP Panel co-chair John Dunne (1st June) or CMIP Panel member Julie Arblaster (8th June) and led by one of the Data Request Task Team co-leads, Martin Juckes (CEDA/STFC, UK) or Chloe Mackallah (CSIRO, AU). During the sessions, attendees were asked to provide feedback on their evolving variable needs (in terms of complexity, volume, and timing) with respect to increasingly complex models. There were also discussions on meeting the demands of downstream users, and the capacity of models and workflows to support those needs. These sessions aimed to facilitate community discussion on variable requirements as planning for CMIP7 gets underway.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.205 | 0.161 |
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".