Bipartite networks improve understanding of effects of waterbody size and angling method on angler–fish interactions
Bibliographic record
Abstract
Networks used to study interactions could provide insights to fisheries. We compiled data from 27 297 interviews of anglers across waterbodies that ranged in size from 1 to 12 113 ha. Catch rates of fish species among anglers grouped by species targeted generally differed between angling methods (bank or boat). We constructed angler–catch bipartite networks (angling method specific) between anglers and fish and measured several network metrics. There was considerable variation in networks among waterbodies, with multiple metrics influenced by waterbody size. Number of species-targeting angler groups and number of fish species caught increased with increasing waterbody size. Mean number of links for species-targeting angler groups and fish species caught also increased with waterbody size. Connectance (realized proportion of possible links) of angler–catch interaction networks decreased slower for boat anglers than for bank anglers with increasing waterbody size. Network specialization (deviation of number of interactions from expected) was not significantly related to waterbody size or angling methods. Application of bipartite networks in fishery science requires careful interpretation of outputs, especially considering the numerous confounding factors prevalent in recreational fisheries.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".