Ensemble numerical forecasts of the sporadic Kuroshio water intrusion (kyucho) into shelf and coastal waters
Bibliographic record
Abstract
The finite volume coastal ocean model downscaling ocean reanalysis and forecast data provided by the Japan Coastal Ocean Predictability Experiment (JCOPE2) are used to forecast sudden Kuroshio water intrusion events (kyucho) induced by frontal waves amplified south of the Bungo Channel in 2010. Two-month hindcast computations give initial conditions of the following 3-month forecasts computations which consist of ten ensemble members. The temperature time series computed by these ten members are averaged to compare with that actually observed in the Bungo Channel, where sudden temperature rises related to kyucho events are remarkable in February, August, and September. Overall, the intense kyucho events actually observed in these months are predicted successfully. However, intense kyucho events are forecasted frequently during the period of May through June even though intense kyucho events are absent during this period in the actual ocean. It is suggested that the present downscaling forecast model requires reliable lateral boundary conditions provided by JCOPE2 data to which numerous Argo data are assimilated to enhance the accuracy. In addition, it seems likely that the model accuracy is reduced by small eddies moving along the shelf break.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".