How do weather systems affect Rainbow Trout (Oncorhynchus mykiss) recreational fishing catch rates, on the Columbia River in Castlegar, British Columbia?
Bibliographic record
Abstract
The Goal of my research was to determine if specific weather patterns share a relationship with Rainbow Trout catch rates within the Columbia River drainage system. To provide greater knowledge for future management strategies as well as more opportunities for recreational anglers to find success. Partnered alongside Ty Tarvd, the research we conducted followed similar guidelines to those performed by John Follows (2019) and Ian Crystal (2019) on the environmental factors affecting Trout feeding habits on the Columbia river between Hugh Keenleyside Dam in Robson and Trail, British Columbia. During the course of their study, John and Ian captured two Rainbow Trout during a below average fall season temperature gradient. They provided a baseline to follow in order to accurately acquire informative data for potential comparisons. This study provides data to the public for informative purposes to which they may better manage or angle for Rainbow Trout along the Columbia River.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".