Mutli-scale variations of ocean temperature off the coast of Nova Scotia: Analyses of in situ and remote sensing observations and high-resolution ocean models towards applications in ecosystem and fishery
Bibliographic record
Abstract
This talk was given at the 25th International SST Users’ Symposium and GHRSST Science Team Meeting (GHRSST25) held in Montreal, Canada/Online from 10 – 14 June 2024. Explore the full program at GHRSST25 on the GHRSST Website here. Abstract Ocean temperature variations off the coast of Nova Scotia are quantified through analyses of data from in situ and satellite remote sensing observations, and high-resolution numerical ocean models. The analyses reveal significant variations at various time-space scales, including: 1) rapid cooling in nearshore waters associated with extreme cold-air outbreaks; 2) frequent cold spells at seabed along the coast from late fall to early spring; 3) large-scale cooling or warming spanning over a season or longer; 3) extensive upwelling along the coast from late-spring to fall; 4) space-time (seasonal and interannual) variations of marine heat waves and cold spells at surface and in the water column; and 5) interannual variations of upwelling along the coast. The forcing mechanisms and predictability of these variations are explored through analysis of atmospheric forcing and ocean model solutions. The potential relevance to applications in marine ecosystems and fishery is discussed.
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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 | 0.000 | 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".