Bioenergetic Habitat Modeling and Food Delivery for Drift Feeding Fishes in Streams
Bibliographic record
Abstract
Environmental and ecological impacts must be considered when designing any engineering works.In rivers, streams and other aquatic systems, this, in part, means identifying changes to physical and chemical parameters affecting habitats of the aquatic community.Due to Provincial and Federal legislation, fish habitat is typically isolated as the key indicator of aquatic impacts as a consequence of instream works.Understanding where fish locate themselves, and why, makes evaluating the impacts and restoration of streams more efficient and effective.One way to predict where fish habitat is optimal is to identify locations in the stream where energetic costs are low and energetic benefits (food) are high for a given fish type.This bioenergetic approach to modeling provides a physically-based quantification of the total amount and quality of fish habitat available under different flow conditions and stream configurations.For drift feeding fishes, such as salmon or trout (important fisheries), inputs to this type of model include stream velocities (from hydraulic models) and the number of invertebrates drifting in the water column within range of the fish location.Previous field investigations have shown that the number of invertebrates drifting is heterogeneous over space and time.In numerical modeling discussed in this chapter, invertebrate start position and
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".