Interannual Transport Variations in the California Undercurrent Off Vancouver Island: Roles of Remote Coastal Sea Level Variability and El Niño
Bibliographic record
Abstract
Abstract The California Undercurrent is a dominant flow feature and has large impacts on regional ecosystems off the west coast of Canada. So far there is limited knowledge on their interannual transport variations. In this paper a high‐resolution ocean circulation model in the northeast Pacific has been established to investigate seasonal and interannual transport variability in the California Undercurrent off Vancouver Island (VI) over 1993–2020. The model forcing includes winds, heat flux, ocean tides, and river runoffs. The model monthly temperature, salinity, and currents are in good agreement with observations at two long‐term monitoring site off West VI. Seasonally the California Undercurrent transport increases from spring to fall and decreases in winter. Interannually the transport anomalies of the California Undercurrent are positively correlated with the inflow through the model southern boundary off Oregon and with the Oceanic Niño Index. It is argued that the interannual changes of the California Undercurrent off VI are likely associated with the sea level variability off South California propagating poleward, providing the poleward longshore pressure gradient along the upper continental slope. El Niño enhances the California Undercurrent mainly due to the equatorial coastal sea level variation propagating poleward.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".