The use of orthoimagery and stereoscopic aerial imagery to identify muskrat ( <i>Ondatra zibethicus</i> ) houses
Bibliographic record
Abstract
Abstract The muskrat ( Ondatra zibethicus ) is considered a ubiquitous inhabitant of wetlands across Canada and the United States, but recent studies indicate that muskrat populations in many parts of North America have experienced substantial declines over the last 40–60 years. Monitoring of muskrat abundance is therefore an important task for wildlife managers, but traditional methods such as house counts conducted during ground‐based surveys can be labor‐intensive and time‐consuming. Poor conditions or a lack of access may limit how much of a wetland can be surveyed. Aerial imagery has previously been used to census a diverse array of wildlife populations but is not yet a common tool for muskrat surveys. To investigate the accuracy of this alternative survey method, we collected aerial imagery from coastal wetlands along the north shore of Lake Ontario during the winter of 2014 for examination in both 2D orthorectified and 3D stereoscopic formats. We compared muskrat house counts obtained from imagery to counts recorded by ground survey crews in the same wetlands during the same winter. We found no significant difference between mean muskrat house counts obtained by ground survey crews and orthoimagery observers. In contrast, stereoscopic imagery observers overestimated mean house counts compared to ground survey crews, which we interpret was due to an increase in false positives. Our results indicate that orthoimagery is a promising tool for assessing muskrat occupancy, provides comparable broad‐scale results to traditional ground survey methods, and may be preferable to wildlife managers for a variety of reasons.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".