Modelling fish physico-thermal habitat selection using functional regression
Bibliographic record
Abstract
In this paper, a new fish habitat modelling approach is introduced using the full probability density functions (PDF), rather than single measurements or central tendency metrics, to describe each predictor. To model habitat selection using PDFs, functional regression models (FRM) are used to allow for the inclusion of curves or functions (smoothed empirical PDFs) in regression models compared to scalars or vectors in classical contexts. The benefits of FRM are exemplified by comparing results with those obtained using generalized additive models (GAM), one of the most recent and performing models in the field. Abundance of juvenile Atlantic salmon sampled at 26 sites (75 m-long x river width) of the Sainte-Marguerite River (Quebec, Canada) was modelled with PDFs of four potential predictors: flow velocity, water depth, substrate size and water temperature. The latter has been less frequently used in habitat modelling and the results showed that it was the most significant predictor. Overall, FRM explained more of the variability in habitat selection than GAM (+14.9% for fry and +8.1% for 1+ parr), mainly due to their ability to use complete distributions of the habitat variables rather than aggregated values (mean). A leave-one-out cross validation showed that both GAM and FRM had similar performance to predict fish abundance. The use of FRM in fish habitat modelling is innovative and its potential should be further developed, especially in the current context where habitat variables are becoming increasingly easy to obtain due to rapid progress of remote measurement techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".