Predicting distribution, production, recreational fishing demand, and conservation concern of Indigenous rainbow trout populations based on landscape characteristics
Bibliographic record
Abstract
Landscape ecology has mainly been integrated in aquatic science to describe patterns and processes in stream networks, but many lakes are connected through their tributaries and are also impacted by their position and connectivity within the watershed. This information on lake characteristics can be used by inland fisheries managers that oversee large landscapes comprising many waterbodies to predict (i) species composition; (ii) population dynamics and productivity; (iii) recreational fishing pressure; and (iv) overall conservation concern. We developed a methodology to assess these four items for the rainbow trout (Oncorhynchus mykiss) fishery of British Columbia by presenting a case study focused on the Clearwater and North Thompson watersheds using the following: the connectivity of lakes within the stream network to predict rainbow trout presence, stream order and lake area to estimate habitat availability and predict population dynamics and productivity (supply), and travel time from population centres to predict recreational fishing pressure (demand). By incorporating connectivity and environmental proxies of habitat, we explore patterns in population dynamics that can be used by fisheries managers to identify populations sensitive to overfishing or disturbance.
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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 | 0.000 | 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) | 0.001 | 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".