SOURCES OF HETEROGENEITY BIAS WHEN DNA MARK-RECAPTURE SAMPLING METHODS ARE APPLIED TO GRIZZLY BEAR (URSUS ARCTOS) POPULATIONS
Bibliographic record
Abstract
Abstract One of the challenges in estimating grizzly bear (Ursus arctos) population size using DNA methods is heterogeneity of capture probabilities. This study developed general tools to explore heterogeneity variation using data from a DNA mark-recapture project in which a proportion of the bear population had GPS collars. The Huggins closed population mark-recapture model was used to determine if capture probability was influenced by sex or collar status. In addition, trap encounter rates were estimated by comparing the closest distance from traps where hair was snagged of bears that were captured, with bears for which we had radiolocations but were not captured. Results of the Huggins analysis suggested that sex, distance of bear DNA capture from grid edge, and whether a bear was radiocollared potentially affected capture probabilities. The encounter rate analysis estimated that 63% of bears that encountered traps were snagged, and that males encountered more traps than females. The following conclusions arise from this study. First, the distance of DNA capture of bears relative to the grid edge should be modeled as an individual covariate to ensure robust estimates of superpopulation size when closure violation is suspected. Second, sampling should be intensive to minimize heterogeneity and to ensure all bears encounter traps. Finally, estimators that are robust to heterogeneity variation should be used, given the various sources of heterogeneity variation.
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 | 0.075 | 0.257 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".