Rainbow trout reproduction data from 3 sampling trips (2018-2019) within Glen Canyon, AZ
Bibliographic record
Abstract
These data were compiled to describe population reproductive structure of rainbow trout in the Colorado River downstream of Glen Canyon Dam, Arizona. Objective(s) of our study were to describe the population on reproductive structure of the rainbow trout population in Glen Canyon on the Colorado River and evaluate the accuracy of nonlethal methods (manual expression and ultrasonography) for assigning sex and reproductive phase. These data represent samples collected in 3 sampling trips fall in October to November 2018, February 2019, and April 2019. These data were collected in Glen Canyon, from Glen Canyon Dam to Lees Ferry on the Colorado River. Specifically, we sampled three reaches within Glen Canyon to represent the upper (A: 2.0 - 4.8 km downstream of the dam), middle (B: 8.7 ? 11.2 km), and lower (C: 18 ? 20.7 km) sections of the tailwater, representing a little more than 1/3rd of the 25-km length of Glen Canyon. These data were collected by the study authors (Crossman, Webb, and Korman) as part of ongoing USGS Trout Recruitment and Growth Dynamics sampling trips in 2018 and 2019. Rainbow trout were sampled from each sampling site on each trip and approximately equal numbers taken from each of four size classes (100-199, 200-299, 300-399, 400-499 mm fork length). Fish were measured for length and weighed and a piece of gonad tissue was preserved for histological assignment in the lab. These data can be used to describe population reproductive structure of rainbow trout, understand less invasive methods for assigning sex, and improve our understanding of how somatic growth and energetic status influence population reproductive structure, reproductive seasonality, and later recruitments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".