Informative priors assess tradeoffs between mark–recapture and telemetry-based fish movement in a large river system
Bibliographic record
Abstract
Telemetry and mark–recapture provide movement information, but each approach comes with tradeoffs, potentially producing conflicting understandings of fish movement patterns. Using a Bayesian framework that allows exchanging priors from either method may help assess these inconsistencies. We evaluated channel catfish Ictalurus punctatus movements in the Red River of the North and Lake Winnipeg system, which impacts harvest management across different jurisdictions and affects different ecosystems (e.g., lotic and lentic). Channel catfish were tagged with T-bar tags or acoustic transmitters. The resulting movement data were modeled using a Bayesian multi-state Cormack–Jolly–Seber model to estimate survival, movement, and recapture probabilities. Model estimates with uninformative priors showed a greater tendency of downstream movement from the Red River into Lake Winnipeg for the T-bar tags. In contrast, the telemetry method showed fish predominantly stay in the river. However, exchanging increasingly stronger prior information from the alternative method's model revealed that telemetry movement estimates were less sensitive than the T-bar model to informative priors. Using priors from both methods provided a transparent means to assessing tagging approach tradeoffs quantitatively.
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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 | 0.034 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".