Partial migration and spawning movements of humpback chub in the Little Colorado River are better understood using data from autonomous PIT tag antennas
Bibliographic record
Abstract
Choosing whether or not to migrate is an important life history decision for many fishes. Here we combine data from physical captures and detections on autonomous passive integrated transponder (PIT) tag antennas to study migration in an endangered fish, the humpback chub (Gila cypha). We develop hidden Markov mark–recapture models with and without antenna detections and find that the model fit without antenna detections misses a large proportion of fish and underestimates migration and survival probabilities. We then assess survival and growth differences associated with life history strategy and migration for different demographic groups (small male, small female, large male, large female). We find large differences in survival according to life history strategy, where residents had much lower overwinter survival than migrants. However, within the migratory life history strategy, survival and growth were similar for active migrants and skipped migrants for all demographic groups. We discuss some common challenges to incorporating detections from autonomous antennas into population models and demonstrate how these data can provide insight about fish movement and life history strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".