An integrated model decomposing the components of detection probability and abundance in unmarked populations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Accurate estimates of population abundance are essential to both theoretical and applied ecology. Rarely are all individuals detected during a survey and abundance models often incorporate some form of imperfect detection. Detection probability, however, consists of three components: probability of presence during a survey, probability of availability given presence, and probability of detection given availability and presence. We develop an integrated model to separate these three detection components and provide abundance estimates for the available, present, and superpopulation of individuals. Our framework integrates several common survey methods for unmarked populations: spatially and temporally replicated counts, distance sampling data, and time‐of‐detection data. Simulations indicated relatively unbiased estimates for detection and availability probabilities. Negative bias in estimated superpopulation abundance was present with three temporally replicated surveys, but greatly reduced with six surveys. In a case study of Island Scrub‐Jays ( Aphelocoma insularis ), posterior modes for presence, availability, and detection probabilities were 0.78, 0.96, and 0.26, respectively, from 10‐min point counts repeated at 97 sites on three occasions, with noticeable differences among available, present, and superpopulation abundance estimates. This generalizable framework integrates common sampling protocols and provides joint inferences on the components of detection probability, spatial and non‐spatial temporary emigration, and abundance in unmarked populations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it