Bibliographic record
Abstract
Abstract. Decadal-scale oceanographic, environmental, and ecological changes have been reported in the Salish Sea, an ecologically productive and biodiverse inland sea in the northeast Pacific that supports the economies and cultures of millions of people; however, there are substantial observational gaps pertaining to physical water properties that make linkages between physical drivers and ecosystem effects difficult to ascertain. With the aim of addressing these gaps, we present the Hindcast of the Salish Sea (HOTSSea) v1 with temporal coverage from 1980–2018, developed using the NEMO ocean engine. An inter-model comparison and preliminary evaluation was performed to assess sensitivity to different atmospheric and ocean reanalysis products used for boundary forcings. Biases inherited from forcings were quantified and the effectiveness of a simple temperature bias correction factor applied at one ocean boundary was evaluated. Evaluation of salinity and temperature indicates performance is best in the Strait of Georgia where the model simulates temperature anomalies and a secular warming trend over the entire water column in general agreement with observations. Analyses of modelled ocean temperature trends throughout the northern and central part of the domain where model skill was high and where observations are relatively sparse yielded fresh insights, including that ocean temperature trends are spatially and temporally variable. HOTSSea v1 will support development of an end-to-end spatial-temporal ecosystem model for the Strait of Georgia and has potential for other research and management applications related to decadal-scale climate effects on marine ecosystems, fish, and fisheries.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.019 | 0.010 |
| Insufficient payload (model declined to judge) | 0.211 | 0.168 |
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".