Estimating disperser abundance using open population models that incorporate data from continuous detection passive integrated transponder arrays
Bibliographic record
Abstract
Autonomous passive integrated transponder (PIT) tag antenna systems continuously detect individually marked organisms at one or more fixed points over long time periods. Estimating abundance using data from autonomous antennas can be challenging because these systems do not detect unmarked individuals. Here we pair PIT antenna data from a tributary with mark–recapture sampling data in a mainstem river to estimate the number of fish moving from the mainstem to the tributary. We then use our model to estimate abundance of non-native rainbow trout (Oncorhynchus mykiss) that move from the Colorado River to the Little Colorado River, the latter of which is important spawning and rearing habitat for federally endangered humpback chub (Gila cypha). We estimate that 226 rainbow trout (95% confidence interval: 127–370) entered the Little Colorado River from October 2013 to April 2014. We discuss the challenges of incorporating detections from autonomous PIT antenna systems into mark–recapture population models, particularly in regards to using information about spatial location to estimate movement and detection probabilities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".