Understanding Atlantic multi‐decadal variability prediction skill
Bibliographic record
Abstract
Initialized and uninitialized decadal retrospective forecasts (re‐forecasts) are used to assess the key regions providing multi‐year prediction skill of the Atlantic multi‐decadal sea surface temperature variability (AMV) and to address the relative roles of the initial conditions and external forcing on this skill. The results show that there is a decay in the AMV skill with forecast time, which is likely to be driven by skill degradation in predicting the AMV subpolar branch due to the lack of skill in predicting the subtropical branch. An important role of the varying radiative forcing in the AMV‐related prediction skill is found over the Labrador and Irminger deep convection regions. Initialized predictions show the largest impact on the improvement in the AMV‐related skill over the area where the Atlantic subpolar gyre operates. Initialization appears also to correct an unrealistic anticorrelation between the AMV phase and the Gulf Stream found in the uninitialized re‐forecasts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".