Rapid drops of ocean temperatures in several shallow bays in Nova Scotia during a recent cold air outbreak
Bibliographic record
Abstract
Abstract From February 3-5, 2023 Atlantic Canada experienced an extremecold Arctic air outbreak with high winds that set many local meteor-ological records, including wind chills as low as -47°C. The impacts of the cold air outbreak on ocean temperatures and ice formation are investigated using predictions from the Coastal Ice-Ocean Prediction System for the East Coast of Canada (CIOPS-E), developed by Environment and Climate Change Canada. Observations from moorings further inform the predictions. The analysis suggests that during the event, several shallow bays and coastal areas in Nova Scotia experienced significant, abrupt temperature decreases ranging from 1.0 to 3.9°C. Overall cooling estimated by the model was 2.3°C for nearshore locations where observations recorded an average temperature decrease of 3.0°C. On the other hand, at individual sites the difference between observed and modelled cooling was up to 2.4°C. This difference can be attributed partly to the fact that the model does not adequately resolve the coastline features of some of the nearshore mooring sites. In comparison, we also analyze another cold air outbreak that occurred on December 15-18, 2016, with wind chills as low as -37°C. The ocean temperatures changes associated with this earlier event saw decreases of 0.8 to 3.5°C, potentially contributing to an ongoing fish kill in St. Mary’s Bay (near the mouth of the Bay of Fundy) at the time. Keywords: Ocean temperature, Cold air outbreak, Ocean forecasting, Shallow bays, Nova Scotia
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".