Seasonal Water Mass Analysis for the Straits of Juan de Fuca and Georgia
Bibliographic record
Abstract
A quantitative analysis of water masses in the coastal waters of southern British Columbia is performed with the Optimum Multiparameter (OMP) analysis method that optimizes the use of a hydrographie dataset by solving an over‐determined linear set of mixing equations. The method is applied to a seasonal dataset collected over five years in the Strait of Georgia, a large semi‐enclosed coastal basin, as well as in Juan de Fuca Strait, its main connection to the Pacific Ocean. Abundant freshwater discharge into the coastal basin forces an estuarine exchange with oceanic shelf water. Six water characteristics of five source water types are used to obtain mixing proportions over the estuary for each of the four seasons. The model results are found to corroborate known aspects of the local dynamics such as the presence of a deep shelf inflow into Juan de Fuca Strait and of the Columbia River plume in winter at the mouth of the strait. The analysis also quantifies lesser known features of the region, such as the characteristics of the mid‐depth intrusions within the Strait of Georgia and the marked effect of remineralization on nutrient distributions in the deep water of the coastal basin.
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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 | 0.001 | 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".