Fish habitat modeling: from conceptual framework to functional tools
Bibliographic record
Abstract
Natural and anthropogenic perturbations do not always equally affect all parts of an ecosystem, and all parts of an ecosystem do not equally contribute to maintain fish communities. The increasing pressure to use natural resources and to modify habitats led to the development of approaches to identify areas of key importance for fish communities. Following these approaches, aquatic systems could be perceived as puzzles, composed of a multitude of pieces with temporally flexible physical attributes and biological roles. Such a spatially explicit framework requires models that may allow one to predict fish distribution patterns and fish net energy gain once they have adopted a specific distribution pattern. Despite the conceptual appeal of spatially explicit approaches, functional tools may be obtained only after their assumptions have been tested and their models have been validated. Efforts must be deployed to identify temporal and spatial scales at which fish distribution and abundance should be estimated and modeled. Studies on fish behaviour and the energetic consequences of these behaviours must be conducted to insure that bioenergetic criteria used to define fish habitat quality do not depend on arbitrary assumptions about fish activity costs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".