Stream fish Bayesian size spectrum model
Bibliographic record
Abstract
Biomass size spectra are useful tools for ecologists to investigate macroecological processes such as trophic energy transfer and productivity. However, little is known about how different methods of aggregating data across spatial scales of river networks may affect community size spectra results. We used size-binned data (0 – 2048 g) of fish assemblages from three Lake Ontario watersheds to compare fish size spectra slopes across multiple stream classification systems and the effects of sampling design on size spectra at broader spatial scales. The slope of individual site-based size spectra ranged from -2.901 to -1.382 (median -1.718) while watershed-level size spectra had an average slope of -1.77. Size spectrum slopes did not differ across stream classes, though sites with salmonid species exhibited less negative slopes. Aggregated size spectra showed better model fit than individual site models regardless of stream order. Precision improved with stratified random sampling and larger sample sizes (>15 sites) at the watershed scale. Aggregating sites using different strategies offers effective approaches for modeling size spectra, supporting investigations into macroecological processes in river ecosystems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".