Trends in body condition of smallmouth bass and northern pike (1982–2013) following multiple ecological perturbations in the St. Lawrence River
Bibliographic record
Abstract
Body condition indices can be used to investigate fish response to biological or environmental change. We used quantile regression to analyze a 32-year weight–length data set (1982–2013) for smallmouth bass (Micropterus dolomieu) and northern pike (Esox lucius) from the St. Lawrence River to examine changes in condition (weight-at-length) during three ecologically distinct time periods. Condition of both species varied among time periods, with smallmouth bass experiencing greater proportional changes than northern pike. Smallmouth bass weight-at-length was greatest during 2005–2013 and lowest during 1982–1992. Northern pike weight-at-length was greatest during 2005–2013 for quantiles ≥0.5, similar between the 1982–1992 and 2005–2013 time periods for lower quantiles, and lowest during 1993–2004. The most likely weight–length model for smallmouth bass included terms for length, presence of round goby (Neogobius melanostomus), presence of dreissenid mussels, and an interaction between length and dreissenid mussels (wi = 0.93). The most likely model for northern pike included terms for length, presence of round goby, presence of dreissenid mussels, water temperature, conspecific abundance, and sex (wi = 0.56). Invasive species and environmental change related to invasive species have altered the condition of smallmouth bass and northern pike in the St. Lawrence River.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".