Science on the edge of spatial scales: a reply to the comments of Williams (2001)
Bibliographic record
Abstract
Guay et al. (2000) evaluated the ability of a numerical habitat model (NHM) to predict the spatial distribution of juveniles of Atlantic salmon (Salmo salar) in a river. The NHM that we used consisted of a hydrodynamic model predicting the physical characteristics (current velocity, water depth, substrate composition) of habitats under any given flow and a biological model assigning an ecological value ranging from 0 (poor habitat) to 1 (excellent habitat) to habitats using their expected physical attributes as independent variables. The main objective of Guay et al. (2000) was to compare the predictions of NHM based on two biological models to the spatial distribution of fish in a river. The new biological model that we developed, the habitat probabilistic index (HPI), predicted a significantly larger fraction of the local variations of fish density (r2 = 0.86) than the traditional habitat suitability index (HSI; r2 = 0.39). Williams (2001) questioned several methodological and fundamental aspects of the work of Guay et al. (2000). The points raised by Williams (2001) about Guay et al. (2000) can be grouped into problems of wording, problems of sampling sufficiency, and problems of spatial scales.
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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 | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.006 | 0.025 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.040 | 0.099 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".