Landscape-scale social and ecological outcomes of dynamic angler and fish behaviours: processes, data, and patterns
Bibliographic record
Abstract
The first relatively complete landscape-scale social–ecological system (SES) model of a recreational fishery was developed and ground-truthed with independent angling effort data. Based on the British Columbia multistock recreational fishery for rainbow trout (Oncorynchus mykiss), the model includes hundreds of individual lake fisheries, hundreds of thousands of anglers, originating from tens of communities, connected by complex road and trail networks, all distributed over a landscape of approximately half a million square kilometres. The approach is unique in that it incorporates realistic and empirically derived behavioural interactions within and among the three key components of the SES: angler communities, fish populations, and management policies. Current management policies were characterized and alternate policies assessed by simulation. We examined spatial patterns in ecological and social properties of the SES and used simulations to investigate the impacts of alternate management policies on these patterns. Simulation outcomes strongly depended on the spatial redistribution of anglers across the landscape, existing road networks, heterogeneity in angler behaviours, and the spatial pattern of fish population productivity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".