Effect of the spatial arrangement of habitat patches on the development of fish habitat models in the littoral zone of a Canadian Shield lake
Bibliographic record
Abstract
We developed fish habitat models in a Canadian Shield lake using (i) a sampling-site approach based on analytical units having a surface area equal to that of sampling sites (S ~ 200 m2), (ii) a constant-multiple approach in which the analytical units constituted grouping of adjacent sampling sites in units of increasing sizes (e.g., 2S or 3S), and (iii) a habitat-patch approach in which only contiguous sampling sites with similar environmental characteristics were merged. The best models explaining within-lake variations in fish density, biomass, and community structure on the littoral zone were obtained using the constant-multiple approach, but the predictive power of these models was highly variable (0 < R2 < 0.9) compared with the habitat-patch approach (0.27 < R2 < 0.49). For these approaches, intrinsic variables (estimated inside the analytical units) explained on average 16%27% of the variations of fish descriptors compared with 6%32% for extrinsic variables (observed outside analytical units or related to the spatial arrangement of habitat characteristics). Our study suggests that habitat patches are reliable analytical units with which to develop fish-habitat models. Our study also indicates that inclusion of variables that refer to landscape characteristics may significantly improve the predictive power of fish habitat models.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".