Striped bass population genetic structure and bait panel development for mixed stock analysis
Bibliographic record
Abstract
The delineation of genetic stock structure and population connectivity are key components in the effective management of exploited fishes, and in preserving the biocomplexity of populations which is critical for maintaining a species resilience to environmental and anthropogenic pressures. The information gained from identifying the genetic structure among populations is important for ensuring that the spatial scale of management makes biological sense, for identifying genetically compatible individuals to be used in stocking and supplementation efforts, and for use in population assignment methods. This body of research focuses on delineating the genetic stock structure of Striped Bass (Morone saxatilis) and building a genetic panel capable of assigning unknown individuals to a population of origin, in order to provide a highly accurate tool for fisheries management. In Chapter 1 I determine the population genetic structure among nine spawning locations of striped bass in the US and Canada and evaluate the power of my genetic data to assign individuals to their spawning river of origin. In Chapter 2, I build and validate a sequence capture panel to be used for conducting mixed stock analyses on striped bass.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".