Assessment of the transferability of biological habitat models for Atlantic salmon parr (<i>Salmo salar</i>)
Bibliographic record
Abstract
We assessed the transferability of the habitat suitability index (HSI) and the habitat probabilistic index (HPI) between two rivers. Transferability was measured by the ability of HSI and HPI models developed in the Sainte-Marguerite River to predict the distribution of Atlantic salmon parr (Salmo salar) in the Escoumins River. HSI and HPI were based on the pattern of utilization by fish of water depth, current velocity, and substrate size. HSI was developed using the preference curve approach, and HPI was developed using a multiple logistic regression. Predicted values of HSI and HPI in Escoumins River ranged from 0 (poor habitat) to 1 (excellent habitat). Fish density in habitat patches assigned different HSI or HPI values ranged from 0 to 1 fish·100 m–2. Only HPI adequately predicted local variations in parr density (r2 = 0.84) in habitat patches of Escoumins River. Our results suggest that HSI is less transferable between rivers than HPI. Differences in substrate size between the two rivers is suspected to impede the transferability of the HSI model. We also argue that the mathematical structure of HPI provides a larger degree of flexibility that facilitates its transferability and its potential generalization.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".