Simulation and empirical analysis of novel sibship-based genetic determination of fish passage
Bibliographic record
Abstract
We develop and test a new analytic approach, termed “sib-split”, to detect fish passage through road crossings. This new approach is based on the genetic analysis of full-siblings on opposite sides of potential barriers. We used simulations and data from two empirical case studies involving brook trout (Salvelinus fontinalis) movement with respect to barriers that varied in strength of effect on fish passage. Simulations revealed that both sib-split and the population assignment-based method (STRUCTURE) were highly accurate (mean accuracy > 99%) under easy-to-detect conditions (moderate to strong genetic differentiation and no movement). However, under difficult-to-detect simulated conditions (no genetic differentiation, 10% movement each generation), sib-split had higher accuracy (mean accuracy = 98%) than STRUCTURE (mean accuracy = 84%). Sib-split also outperformed STRUCTURE (mean accuracy 98% versus 89%) under a more difficult-to-detect simulated management scenario (simulated construction of a new complete barrier to movement). Sib-split provided more reliable and easily interpretable movement detection in both easy- and difficult-to-detect empirical case studies. With the empirical case studies, sensitivity to the prior on migration rate precluded use of STRUCTURE by itself, but a two-step approach where sib-split results were used to provide an informed migration prior for STRUCTURE provided additional information for both case studies.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".