Modelling complex spatial–temporal drivers of habitat suitability for an imperilled stream fish
Bibliographic record
Abstract
Abstract Fish populations rely on complex environmental conditions involving physical, chemical, and biological factors. Understanding the factors that control population persistence and productivity is essential for species management. We assessed the distribution and associated habitat features of a species at risk in Canada, Silver Shiner ( Notropis photogenis ), within Sixteen Mile Creek, a tributary of Lake Ontario. Using random forest models, we quantified a range of ecological factors ( n = 25) to estimate habitat associations for sampled populations and life stages (juvenile, adult). A complex set of ecological factors were informative predictors of Silver Shiner distribution, including physical (stream morphology, water velocity, substrate type), and biological (aquatic and riparian vegetation) conditions. Juveniles were less responsive to habitat conditions but exhibited high seasonal variability in occurrence. Adults were most common in stream sections with greater than 0.5 m depth and stream velocity less than 0.6 m/s, and areas without silt substrate. Broadly, the models predicted Silver Shiner distribution with 68–92% accuracy in non-training data. Our findings describe the habitat conditions that Silver Shiner currently occupies in an urban drainage, which may serve as a point of reference for habitat protection and restoration. Further, predictive species distribution models can serve to identify habitat for further monitoring and restoration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".