A practical guide for assigning sex and stage of maturity in sturgeons and paddlefish
Bibliographic record
Abstract
The goal of this paper is to improve the assignment of sex and stage of maturity for sturgeons and paddlefish by providing both an overview of the gonadal stages of maturity and general guidelines and training needs for the four most commonly used techniques to assign sex and stage of maturity including ultrasound, endoscopy, plasma sex steroid analysis, and biopsy of the gonads via celiotomy. Sturgeons and paddlefish do not express external sexual dimorphism, which can make assignment of sex and stage of maturity challenging. Correct assignment of sex and stage of maturity is important for management of wild populations as well as for aquaculture, whether for conservation or commercial production. Selecting a technique to use when assigning sex and stage of maturity will depend on a number of factors; a comparison among these techniques is provided in this review, including tradeoffs, to help assist in technique selection based on specific research or production goals. The use of more than one technique may be beneficial to determine error rates associated with a single technique. This review is intended to serve as a practical resource when assigning sex and stage of maturity in field or laboratory settings in addition to stressing the importance of correctly identifying sex during research and in the management of populations.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.039 |
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".