Too many to count: Using orthophotography to census an unharvested beaver (<i>Castor canadensis</i>) population in Ontario
Bibliographic record
Abstract
Abstract Various methods exist to monitor wildlife populations and estimate trends in their distribution and abundance. For American beaver ( Castor canadensis ), aerial surveys provide a means to obtain abundance data over large areas and typically involve observers searching watercourses and shorelines for active beaver presence. Here, we describe a systematic aerial photographic census we designed and executed in autumn to quantify annual beaver colony abundance on the 184‐km 2 Michipicoten Island, Ontario, from 2015 to 2019. Aerial photographs were stitched together into orthophotomosaics after each census and visually searched for beaver food caches, with each food cache representing an independent beaver colony. Our methods revealed that beaver colony abundance declined substantially from a peak of 6.1 colonies/km 2 in 2015, the highest reported across North America, to 0.4 colonies/km 2 in 2018. Beaver abundance remained low through 2019. Although photographing the entire isolated study area required relatively little time and effort, even when beaver density was very high, post‐census processing work was time‐consuming. Lessons learned will improve efficiencies of our future censuses and aid other researchers. Our method is advantageous over traditional aerial wildlife surveys in that it provides a digital and visual record that can be used for additional analyses.
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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) | 0.005 | 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".