A framework for assessing the ability to detect macroscale effects on fish growth
Bibliographic record
Abstract
Various abiotic and biotic factors affect fish and their habitats at macroscales. For example, changes in global temperatures will likely alter demographic rates, including growth. However, to date, there is no statistical framework for assessing the ability to detect macroscale effects on fish growth under different sampling scenarios. We provide a generalized framework for calculating the frequentist and Bayesian power of detecting macroscale effects on fish growth. We illustrate this framework for a range of sampling scenarios that varied in the number of fish sampled per lake, the number of lakes sampled, and the magnitude of the temperature effect on growth for two case study species. However, the framework can be adapted to investigate other species, sampling scenarios, and environmental drivers. The ability to detect macroscale effects was more affected by the number of lakes sampled rather than the number of fish sampled from each lake. Confidently detecting macroscale effects likely requires sampling hundreds of lakes. This was true for both case study species, despite different life histories and extents of spatial variability in growth.
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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 | 0.111 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".