Transactions of the American Fisheries Society 130:878–897, 2001 � Copyright by the American Fisheries Society 2001 Fish–Habitat Relationships in Lakes: Gaining Predictive and Explanatory Insight by Using Artificial Neural Networks
Bibliographic record
Abstract
Abstract.—Understanding and predicting the impacts of habitat modification and loss on fish populations are among the main challenges confronting fisheries biologists in the new millennium. Statistical models play an important role in this regard, providing a means to quantify how environmental conditions shape contemporary patterns in fish populations and communities and formulating this knowledge in a framework where future patterns can be predicted. Developing fish–habitat models by traditional statistical approaches is problematic because species often exhibit complex, nonlinear responses to environmental conditions and biotic interactions. We demonstrate the value of a robust statistical technique, artificial neural networks, relative to more traditional regression techniques for modeling such complexities in fish–habitat relationships. Using artificial neural networks, we provide both explanatory and predictive insight into the whole-lake and withinlake habitat factors shaping species occurrence and abundance in lakes from southcentral Ontario, Canada. The results show that species presence or absence is highly predictable based on wholelake measures of habitat, and that these fish–habitat models show good generality in predicting occurrence in other lakes from an adjacent drainage. Detailed evaluation of these models shows that partitioning the predictive performance of the models into measures such as sensitivity (ability
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.200 | 0.077 |
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".