The Great Whale River ecosystem: ecology of a subarctic river and its receiving waters in coastal Hudson Bay, Canada
Bibliographic record
Abstract
The Great Whale River in subarctic Quebec, Canada, is one of the main freshwater inflows to Hudson Bay. This region is experiencing rapid climate change, with pronounced impacts on the cryosphere, and ongoing socio-economic development that may accelerate with future road and shipping links. This review integrates information available to date on the Great Whale River ecosystem, which we define as the river and its watershed, its source lakes and streams, and the river mouth environment and beyond the shoreline in southeastern Hudson Bay. Our aim was to define the current state of this ecosystem as a baseline for ongoing observations, with emphasis on (1) the distribution of water masses, optical characteristics, freshwater discharge, and ice cover; (2) concentrations and fluxes of carbon, nutrients, and contaminants; (3) abundance, production and diversity of the organisms associated with the sea ice ecosystem; (4) plankton abundance, biomass, production, and assemblages; (5) benthic abundance and diversity; (6) fish abundance, diversity and population dynamics; (7) marine mammal biology; and (8) global change impacts on freshwater and marine habitats. This synthesis provides a first step towards the integrated management of the Great Whale River ecosystem, and for similar freshwater-marine systems in the subarctic region.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".