Mark-recapture with tag loss
Bibliographic record
Abstract
Mark-recapture studies are used to estimate population parameters such as abundance, survival and recruitment. Briefly, animals are captured, marked with an individually identifiable tag and released. First capture provides information about abundance. Subsequent recaptures provided survival information about the individual. One of the fundamental assumptions in mark-recapture studies is that tags are not lost. If this assumption is violated, parameter and standard error estimates are biased. This thesis deals with the analysis of 3 mark-recapture experiments under tag-loss. The second chapter looks at premature radio failure in radio-telemetry studies. Radio-tags, because of their high detectability, are often used in capture-recapture studies. A key assumption is that radio-tags do not cease functioning during the study. Radio-tag failure before the end of a study can lead to underestimates of survival rates. We develop a model to incorporate secondary radio-tag failure data. This model was applied to chinook smolts (Oncorhynchus tshawytscha) on the Columbia River, WA. The third chapter incorporates tag loss into the Jolly-Seber model. Tag loss in the Jolly-Seber model has only been dealt with in an ad hoc manner. We develop methodology to estimate population sizes and tag-retention in double-tagging mark-recapture experiments. We apply this methodology to the study of walleyes (Stitostedion vitreum) in Mille Lacs, Minnesota. Finally, in the fourth chapter, we develop a Poisson migration model incorporating tag loss. This model is applied to the study of yellowtail flounder (Limanda femginea) on the Grand Banks of Newfoundland.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".