Rainbow trout growth data and growth covariate data from Glen Canyon, Colorado River, Arizona, 2012-2021
Bibliographic record
Abstract
These data are the primary data used to model rainbow trout growth in Glen Canyon. Fish growth data were collected from nighttime boat electrofishing field campaigns conducted five to six times per year in April, July, September, and January, from April 2012 through November 2021 for a total of 9798 observations of mark-recapture-based growth. Sampling was conducted in a five km reach in the lower portion of the Glen Canyon tailwater (3.7-8.9 km upstream of Lees Ferry, AZ). Two nights of sampling occurred on each trip, with the central 2-3 km of the reach sampled on both nights. After capture, fish were kept in aerated 40-L buckets and transported to a central processing location. Groups of 10-15 fish were anesthetized and rainbow trout ? 75 mm were scanned and injected with a passive integrated transponders (PIT) tag if they had not been previously tagged. Fork length was measured to the nearest mm, and weight was measured to the nearest gram for fish ? 150 mm and to the nearest 0.1 g for smaller fish. Provided are tabulated data for fish forklength and weight at capture and recapture as well as estimates of rainbow trout biomass at each trip interval. We evaluated the effects of discharge, water temperature, competition for prey, solar insolation, soluble reactive phosphorus concentration, and the presence of absence of two experimental flows on growth rates of rainbow trout. These seven covariates were selected based on findings from previous modeling efforts and hypotheses regarding how experimental flows affect the rate of prey delivery, metabolic and foraging costs, foraging efficiency, and prey availability. Covariates are compiled as tabulated mean values for each reach and sampling trip and corresponding data sources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".