Importance and Predictability of Cannibalism in Rainbow Smelt
Bibliographic record
Abstract
Abstract Cannibalism is a key interaction between young of year (age-0) and older fish in many freshwater ecosystems. Density and spatial overlap between age-groups often drive cannibalism. Because both density and overlap can be quantified, the magnitude of cannibalism may be predictable. Our study considered cannibalism in rainbow smelt Osmerus mordax in Lake Champlain (New York–Vermont, United States, and Quebec, Canada). We used acoustic estimates of the density and distribution of age-0 and yearling-and-older (age-1+) rainbow smelt to predict cannibalism in the diets of age-1+ fish during 2001 and 2002. Experienced density, a measure combining density and spatial overlap, was the strongest predictor (R2 = 0.89) of the proportion of cannibals in the age-1+ population. Neither spatial niche overlap (R2 = 0.04) nor age-0 density (R2 = 0.30) alone was a good predictor of cannibalism. Cannibalism among age-1+ rainbow smelt was highest in June, lowest in July, and high in September owing to differences in thermal stratification and habitat shifts by age-0 fish. Between July and September, age-1+ rainbow smelt consumed 0.1–11% of the age-0 population each day. This resulted in a 38–93% mortality of age-0 fish due to cannibalism. These estimated mortality rates did not differ significantly from observed declines in age-0 rainbow smelt abundances between sampling dates. Age-1+ rainbow smelt are probably the primary predators on age-0 rainbow smelt during the summer and early fall in Lake Champlain.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".