Can photograph identification be a useful tool in the identification of Bighorn Sheep (Ovis canadensis) at the Kootenay Pass Feeding Station, British Columbia?
Bibliographic record
Abstract
The use of photo-identification has been applied to marine mammals such as sea otters (Enhydra lutris), Humpback whales (Megaptera novaeangliae) and have been applied to Polar Bear (Ursus maritimus). Photo identification is the method of taking photographs of a universal marking on a species and using those to identify members of the herd or grouping without being invasive. With the photographs that are begin collected by these studies, researchers can compile these into a database and all researchers would be able to have access to these photos for identification in the field. I wanted to use this method of identification on Bighorn sheep (Ovis canadensis) and determine is this method can be used on wild herds, as well as a way to monitor a species population from a distance. The use of photo-identification on Bighorn sheep will require taking photographs of the rumps and the heads of each member of the herd that is located at the Kootenay Pass feeding station. This herd have been coming to the station for about 40 years and they have become accustom to having humans near, which makes this herd a perfect subject for this study. By testing this method on this herd, we can determine if this technique of identification can be used on wild herds and different species in the long run.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".