Optimization of temporal versus spatial replication in the development of habitat use models to explain among-reach variations of fish density estimates in rivers
Bibliographic record
Abstract
We evaluated the effects of temporal variation of fish density estimates on the explanatory power of habitat use models. Fish density estimates were obtained using visual surveys (10 visits) in eighteen 100 m reaches over a 7-week period. Physical attributes of reaches were estimated. Field data were used to develop a simulation domain (10 000 reaches) that reflected the spatio-temporal variability of fish density estimates and physical attributes. Simulations indicated that for a sampling effort of approximately 200 surveys, the number of reaches surveyed (25 to 200) and the number of surveys per reach (1 to 8) affected the adjusted R2of models by 5% to 42%. The established practice of sampling a maximized number of reaches once did not appear necessarily optimal for developing habitat use models. Analysis of temporal coefficients of variation suggests that species within the same family may require a similar survey design. Hence, for salmonids, it may be more appropriate to sample more reaches once, and for cyprinids, it may be more optimal to repeatedly sample fewer reaches.
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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 | 0.046 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".