DISCUSSION Spatial autocorrelation and fish production in freshwaters: a comment on Randall et al. (1995)1
Bibliographic record
Abstract
graphic areas, sites near one another often have similar lev-els of species abundance or biomass as a result of similar geologic, climatic, or biotic factors (Legendre and Fortin 1989). This positive autocorrelation at “small ” spatial scales means that nearby sites should not be treated as independent replicates in classical statistical analyses (Legendre 1993). Under these conditions, statistical tests are too liberal and prone to type I errors. Despite knowing these pitfalls, many fisheries researchers continue to use classical statistical anal-yses and ignore the potential influence that autocorrelation could have on interpretations of broad geographic scale pat-terns (see Hinch et al. 1994) or temporal patterns (Pyper and Peterman 1998) of fish attributes. In a recent issue of the Canadian Journal of Fisheries and Aquatic Sciences, Randall et al. (1995) used data from entire
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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 | 0.041 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.013 | 0.004 |
| Research integrity | 0.023 | 0.038 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".