Annual recruitment is correlated with reproductive success in a smallmouth bass population
Bibliographic record
Abstract
Annual recruitment in fish is undoubtedly impacted by a vast number of biotic and abiotic factors. That is especially the case for fish species such as the black bass (species in the genus Micropterus), where there is extended parental care. Although much focus has been given in the past on determining the roles that many of these factors (e.g., temperatures, wind, flow rates, and habitat change) play in determining recruitment among the back basses, little attention has been given to assessing what role reproductive success plays in that determination. To address this question, we conducted a long-term study on two adjacent smallmouth bass (SMB) Micropterus dolomieu Lacepède, 1802 populations in eastern ON to assess the relationship between annual fry cohort size (FCS) (i.e., population-wide reproductive success) and annual recruitment. To measure population-wide annual FCS, we used snorkel surveys to conduct a complete census of nesting SMB males during the spawn from 1990 to 2015. During those surveys, we quantified mating success, determined which nests were successful or not, and calculated the number of independent fry produced each year by summing those numbers across all successful nests. Summer snorkel surveys from 1991 to 2016 assessed annual recruitment through visual counts of age 1+ juveniles. Results demonstrated a highly significant, positive, linear relationship between annual FCS and annual recruitment.
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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".