Species–environment relationships, clusters, and thermal ranges of fish species inhabiting boreal rivers
Bibliographic record
Abstract
Understanding how key environmental factors affect fish communities is necessary for sound environmental management. Accordingly, we studied fish species–environment relationships in Finnish boreal rivers. A self-organizing map (SOM) analysis showed strong relationships between the occurrence of 18 fish species and 10 environmental variables (variance explained: 43.4%). In our logistic regression models run for each fish species, the predictive power varied from poor (AUC = 0.67–0.68) to excellent (AUC = 0.96), indicating that the responses were species-specific. The overall best predictors of the species occurrence were the local variables water depth and water temperature, and the regional variables stream size, altitude and annual mean temperature. Our results on fish assemblage types (clusters), identified by SOM, and co-occurrence of fish species, discovered by a hierarchical cluster analysis, reveal insights into the structure of the river fish assemblages. Moreover, our results suggest that the stream size contributes to thermal ranges in which each species is able to occur, which has relevance to predicting the impacts of climate change on riverine fish.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".