Bibliographic record
Abstract
Wildlife cameras allow conservation scientists to collect robust wildlife occupancy data. However, there are limitations associated with wildlife cameras that must be understood prior to their use. This study compared two wildlife camera models, Spypoint Solar Trail and Reconyx Hyperfire 2, on behalf of Calgary Captured, a collaborative project between the Miistakis Institute and the City of Calgary, that aims to determine wildlife occupancy in Calgary’s Natural Area Parks. Cameras were set up in pairs at 10 sites to compare their efficacy in detecting wildlife. There was no significant difference in white-tailed deer (Odocoileus virginianus) or coyote (Canis latrans) detections by the Spypoint and Reconyx cameras, but the Reconyx cameras captured two species, bobcat (Lynx rufus) and deer mouse (Peromyscus maniculatus), that the Spypoint model failed to detect. The Reconyx cameras had fewer trap days because their Nickel Metal Hydride (NiMH) batteries consistently failed due to cold weather, whereas the Spypoint cameras’ solar panel continued to function throughout the study. Nevertheless, the fact that the Reconyx cameras still captured more species than the Spypoint cameras despite fewer trap days indicates that Reconyx Hyperfire 2 is much more effective in occupancy studies than the Spypoint Solar Trail model. Also, the results of this study highlight the importance of choosing appropriate batteries and settings within the model to ensure the successful use of wildlife cameras.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".