COMPARISON OF FIXED-WING AND HELICOPTER SEARCHES FOR MOOSE IN A MID-WINTER HABITAT-BASED SURVEY
Bibliographic record
Abstract
We conducted a mid-winter habitat-based survey in Terra Nova National Park and an adjacent hunted area (Moose Management Area 27) to compare the reliability and accuracy of using fixed-wing and helicopter aircraft for counting moose. Forest inventory mapping was the primary consideration in defining block boundaries because this readily available information could be easily interpreted by observers during aircraft navigation, and because map classes could be chosen in a way expected to reduce variability in moose distribution. Blocks were also classified from forest inventory mapping as being either open (mean crown closure of all stands 50%). We tested the precision of fixed-wing and helicopter aircraft for counting moose in blocks with open and dense crown cover by increasing the time spent during second searches with each aircraft type. More moose were seen in open blocks during second searches with increased flying time in both fixed-wing aircraft (100%) and helicopters (160%) than in dense forest cover blocks (12% and 43%, respectively). We also compared the accuracy of the two aircraft types in each crown cover class by recounting the same blocks at a similar intensity. Verifying the accuracy of fixed-wing counts with helicopter searches of the same 8 blocks (the same crew flew approximately the same time), we found that the helicopter counts were on average 78% higher. We conclude that for highest accuracy and best classification of animals during a moose survey, helicopter counting is superior to fixed wing counting. ALCES VOL. 38: 47-53 (2002)
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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 | 0.001 | 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 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".