Spatial and temporal factors influencing sightability of elk
Bibliographic record
Abstract
Abstract Few tracking studies consider seasonal changes in ability to re‐sight wildlife, despite potential for biases in sightability to mislead our interpretation of models of movement and abundance. We developed seasonal sightability models based on visual observations of radio‐collared elk ( Cervus elaphus ) in Manitoba, Canada, through 6 seasons. We located 377 elk 8,862 times using aerial telemetry from 2002 to 2009. We tested the hypothesis that sites where we were able to visually observe radio‐collared elk during aerial telemetry differed from sites where collared elk were known to be present but could not be sighted. Relationships varied with season and elk sightability was influenced by forest type, habitat openness, distance to edge, and time of day. Our results confirm that observers have the highest probability of detecting elk in early and late winter. However, factors such as day length, which increases by 64% during this period, suggest that fewer impediments to detection exist in late winter. Our findings reinforce the need to account for seasonal as well as spatial changes in habitat‐specific sightability models. © 2011 The Wildlife Society.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".