Development of a Sightability Model for Low‐Density Elk Populations in Ontario, Canada
Bibliographic record
Abstract
ABSTRACT The status of recolonizing elk ( Cervus elaphus ) populations in Ontario, Canada, is unclear and there is a need for effective population survey methods that can be applied locally. We sought to develop a sightability model that could account for both low densities of elk and dense forest cover in elk‐release areas in Ontario. We corrected winter aerial survey counts for sightability based on radiocollared animals known to be within observable distance of the aircraft. The multivariate model with the highest Akaike's Information Criterion corrected for sample size weight ( w i = 0.427) revealed that elk group size, elk activity, dominant tree type, percent canopy cover, and percent conifer cover were significant predictors of elk sightability. The group‐size effect indicated that odds of sighting an elk increased by 1.353 (95% CI = 0.874‐3.689) for every additional elk. Standing elk were 5.033 (95% CI = 0.936‐15.541) times more likely to be observed than were resting elk, and those located in conifer cover were 0.013 (95% CI = 0.001‐0.278) times less likely to be sighted than elk in deciduous cover. Furthermore, elk located in >50% canopy cover and >50% conifer cover were 0.041 (95% CI = 0.003‐0.619) times and 0.484 (95% CI = 0.024‐9.721) times less likely to be sighted than elk in more open habitat, respectively. During model validation, observers detected 79% (113/143) of known elk in any given area, and population and sightability model predictions (±90% CI) overlapped with the population estimate, implying that our predictive model was robust. Unsurprisingly, large groups of elk in open habitat increased model precision, which highlights difficulties of counting Ontario elk in their northern range. We conclude that our model provided increased reliability for estimating elk numbers in Ontario compared to existing methods, and that the estimator may be useful in other areas where elk density is low and sightability is poor due to dense forest cover.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".