The Importance of Including Water Temperature Simulations in a 2D Fish Habitat Model for the St. Lawrence River
Bibliographic record
Abstract
Extreme climatic conditions likely caused a massive fish mortality during the summer of 2001 in the St. Lawrence River. To corroborate this hypothesis, we used a physical habitat simulation approach incorporating hydraulic and water temperature models. Spawning Habitat Suitability Indices (HSI) for common carp (Cyprinus carpio) were developed using fuzzy logic and applied to the model outputs to estimate habitat weighted usable area during the event. The results revealed that areas suitable for common carp spawning (HSI > 0.3) were severely reduced by high water temperatures, which exceeded 28 °C during the mortality event. During the mortality event, the amount of suitable habitat was reduced to <200 ha/day, representing less than 15% of the maximum potential suitable habitat in the study reach. In addition, the availability of cooler habitats that could have been used as thermal refuges was also reduced. These results indicate that the high water temperature in spawning areas and reduced accessibility to thermal refuge habitats exposed the carp to substantial physiological and environmental stress. The high water temperatures were highly detrimental to the fish and eventually led to the observed mortalities. This study demonstrates the importance of including water temperature in habitat suitability models.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".