The first coupled historical forecasting project (CHFP1)
Bibliographic record
Abstract
A set of retrospective multi‐seasonal ensemble predictions based on a coupled global atmosphere‐ocean model is described. These predictions, designated as the first coupled Historical Forecasting Project or CHFP1, are produced with the climate model CGCM3.1 of the Canadian Centre for Climate Modelling and Analysis using a very simple initialization procedure in which model sea surface temperatures (SSTs) are nudged toward the observed values during a multi‐year period preceding the beginning of a forecast. This procedure, in addition to constraining initial SSTs to be close to observations, initializes equatorial Pacific zonal wind stress and thermocline depth with some skill. The ability of the subsequent forecasts to predict the evolution of SSTs, particularly in the equatorial Pacific, and surface air temperatures globally and in Canada is assessed. The results are compared with those of the second Historical Forecasting Project or HFP2, which uses a two‐tier strategy in which model SSTs are externally specified. Skill of CHFP1 forecasts, though generally modest, exceeds those of HFP2 in some cases, despite the larger multi‐model ensemble used in HFP2. CHFP1 represents an initial step in development directed toward a coupled operational seasonal prediction system for Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".