Seasonal forecasts of Arctic sea ice initialized with observations of ice thickness
Bibliographic record
Abstract
Seasonal forecasts of the September 2012 Arctic sea ice thickness and extent are conducted starting from 1 June 2012. An ensemble of forecasts is made with a coupled ice‐ocean model. For the first time, observations of the ice thickness are used to correct the initial ice thickness distribution to improve the initial conditions. Data from two airborne campaigns are used: NASA Operation IceBridge and SIZONet. The model was advanced through April and May using reanalysis data from 2012 and for June–September it was forced with reanalysis data from the previous seven summers. The ice extent in the corrected runs averaged lower in the Pacific sector and higher in the Atlantic sector compared to control runs with no corrections. The predicted total ice extent is 4.4 +/− 0.5 M km 2 , 0.2 M km 2 less than that made with the control runs but 0.8 M km 2 higher than the observed September extent.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".