Diet and divergence of introduced smallmouth bass (<i>Micropterus dolomieu</i>) populations
Bibliographic record
Abstract
We examine the degree and causes of divergence in growth and reproduction in two populations of smallmouth bass (Micropterus dolomieu) introduced a century ago. Despite a common source, the Provoking Lake population now has a higher population density and slower growing individuals than the Opeongo Lake population. Using this system, we test the predictions of life history theory that delayed maturation and reduced reproductive investment are expected in high density populations with slow individual growth rates. Observations on both populations run directly counter to the aforementioned expectations. Instead, Provoking males have smaller sizes and younger ages at nesting and higher gonad masses than Opeongo males; Provoking females have smaller sizes at maturity, larger egg sizes, and higher ovarian dry masses than Opeongo females. Temperature, food availability, diet ontogeny, young-of-the-year mortality, and adult mortality were examined as plausible contributors to the divergence. Results suggest that low food availability, likely caused or mediated by intraspecific competition for prey, and lack of large prey in the diet are contributing to the slow growth, increased reproductive investment, and higher mortality following reproduction in Provoking. This study provides insight into the processes that produce rapid divergence of life history in a species exhibiting parental care.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".