Computing and Representing Sea Ice Trends: Toward a Community Consensus
Bibliographic record
Abstract
Estimates of the recent decline in Arctic Ocean summer sea ice extent can vary due to differences in sea ice data sources, in the number of years used to compute the trend, and in the start and end years used in the trend computation. Compounding such differences, estimates of the relative decline in sea ice cover (given in percent change per decade) can further vary due to the choice of reference value (the initial point of the trend line, a climatological baseline, etc.). Further adding to the confusion, very often when relative trends are reported in research papers, the reference values used are not specified or made clear. This can lead to confusion when trend studies are cited in the press and public reports.
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 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 | 0.116 | 0.275 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.024 | 0.038 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".