Testing a double-count aerial survey technique for White-tailed Deer, Odocoileus virginianus, in Quebec
Bibliographic record
Abstract
Testing a double-count aerial survey technique forWhite-tailed Deer, Odocoileus virginianus, in Québec.Canadian Field-Naturalist 116(3):488-496.In a double-count aerial survey, two independent observers, located on the same side of an aircraft, simultaneously count animals in sample plots.To evaluate if this technique could be implemented as part of our White-Tailed Deer (Odocoileus virginianus) management program, we assessed its precision to estimate densities over large wintering areas (2 25 km", 10 surveys) and whole hunting zones (1600-26 000 km', 14 surveys).We also tested its repeatability by replicating eight surveys two to five times.We finally compared double-count aerial surveys with pellet-group counts, which were previously used to estimate deer numbers.Surveys of large wintering areas indicated that a 90% confidence interval (CI) of + 20% could be obtained with a sample size of 50-100 plots (5 km X 60 m strip plots).In hunting zones, 100-200 plots would have been needed to reach the same precision.Densities from replicated surveys were not considered biologically different (difference > 30% between two replicates within each survey) for 21 of 24 replicates overall.When both techniques were applied to the same wintering areas, the 90% confidence limits of the aerial estimate encompassed the pellet-group estimate in five of nine surveys, and was lower in one survey and was higher in three surveys.Although the costs of the aerial survey and the pellet-group count techniques are rather similar, we suggest that aerial surveys provide better estimates.We conclude that the double-count technique is reliable to survey White-Tailed Deer at a reasonable cost.In our context, a typical zone (200 plots) requires 30-40 helicopter hours and 10 days of work by a three-person crew.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".