Effects of storms, angling, and nest predation during angling on smallmouth bass (<i>Micropterus dolomieu</i>) nest success
Bibliographic record
Abstract
We studied how storms, angling, and nest predation during angling affected smallmouth bass (Micropterus dolomieu) nest survival in the Bass Islands, Lake Erie, Ohio, USA. Increasing angler effort and introduction of an exotic nest predator, round goby (Neogobious melanostomus), have raised concerns about smallmouth bass recruitment in Lake Erie. We surveyed smallmouth bass nests and calculated daily survival rates for nests assigned to different angling treatments: control, angling without predation, or angling with predation treatments. Only 30% of control nests were successful compared with 11% of angling without predation and 14% of angling with predation treatments. We used the Mayfield method and maximum likelihood models in the program MARK to estimate the survival rates for nests of different treatments and exposed to different numbers of storms. Although nest predators consumed about 35% of broods during angling, daily nest survival rates of both angling treatments were similar. Angling reduced nest survival compared with controls by approximately 5%. Storms reduced both nest success and daily survival. The best model predicting daily nest survival included the added effects of angling treatment and number of storms. Thus, whereas storms and angling affected smallmouth bass nest survival, nest predation during angling did not.
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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 | 0.000 | 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".