The effect of cloud cover on the development of habitat quality indices for juvenile Atlantic salmon (<i>Salmo salar</i>)
Bibliographic record
Abstract
We tested the validity of the predictions made by a habitat probabilistic index (HPI) developed using a description of the physical conditions (depth, flow velocity, grain size) used and avoided by parrs during days of different cloudiness. Thirteen surveys were designed to estimate the number and the distribution of parrs actively foraging within a 300-m reach of a river. During these surveys, the number of parrs actively foraging ranged from 12 to 118, cloud cover ranged from 5% to 100%, and water temperature ranged from 16.5 °C to 21.7 °C. The number of parrs actively foraging was negatively related to cloud cover (r2 = 0.44 to 0.88) but was independent of water temperature. HPI models developed under low (<33%) and intermediate (34–67%) cloud cover explained 82% to 98% of the local variations of fish density. The HPI model developed under high cloud cover (67–100%) was unable to predict fish distribution observed during cloudy days. Our results suggest that HPI models developed when cloudiness is >67% may have a limited predictive power.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".