Potvin double-count aerial surveys in New Brunswick: are results reliable for moose?
Bibliographic record
Abstract
Following the rapid decline of deer (Odocoileus virginianus) across northern New Brunswick in the late 1980s, the New Brunswick Department of Natural Resources began to utilize a double-count helicopter survey to estimate deer numbers. Although the survey was designed for deer, moose (Alces alces) sightings were also recorded; however, no analysis was conducted on the accuracy or usefulness of these data to estimate moose numbers. The survey design was a modification of the Potvin double-count survey method for deer which accounts for most caveats to aerial surveys. This double-count (mark-recapture) technique allows calculation of bias for both observers, for single and groups of moose, and individual flights. Moose population estimates calculated from 79 flights ranged from 0.17-3.49 moose/km2 and were similar to a variety of estimates throughout North America. Population estimates from 2004-2009 correlated well with corresponding 2009 population indices for moose based on number of moose seen by deer hunters (Corr. = 0.725, P 0.4 and flights occur before mid-February when moose may occupy denser canopy cover.
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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 | 0.003 | 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) | 0.000 | 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".