Evaluating the utility of effective breeding size estimates for monitoring sea lamprey spawning abundance
Bibliographic record
Abstract
Abstract Sea lamprey ( Petromyzon marinus ) is an invasive species that is a significant source of mortality for populations of valued fish species across the North American Great Lakes. Large annual control programs are needed to reduce the species' impacts; however, the number of successfully spawning adults cannot currently be accurately assessed. In this study, effective breeding size ( N b ) and the minimum number of spawning adults ( N s ) were estimated for larval cohorts from 17 tributaries across all five Great Lakes using single nucleotide polymorphisms (SNP) genotyped via RAD‐capture sequencing. Reconstructed larval pedigrees showed substantial variability in the size and number of full‐ and half‐sibling groups, N b (<1–367), and N s (5–545) among streams. Generalized linear models examining the effects of stream environmental characteristics and aspects of sampling regimes on N b and N s estimates identified sample size, the number of sampling sites, and drainage area as important factors predicting N b and N s . Correlations between N b , N s , and capture–mark–recapture estimates of adult census size ( N c ) increased when streams with small sample sizes ( n < 50) were removed. Results collectively indicate that parameters estimated from genetic data can provide valuable information on spawning adults in a river system, especially if sampling regimes are standardized and physical stream covariates are included.
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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 | 0.003 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".