USING A DOUBLE-COUNT AERIAL SURVEY TO ESTIMATE MOOSE ABUNDANCE IN MAINE
Bibliographic record
Abstract
Management goals and objectives for moose ( Alces alces ) in Maine are centered on providing hunting and wildlife viewing opportunity. Robust population estimates of moose are critical to assure that harvest rates are appropriate and biologically sustainable while also addressing values of other user groups. The Maine Department of Inland Fisheries and Wildlife most recently used the relationship between moose sightings by deer hunters and moose abundance to produce density indices within Wildlife Management Districts (WMD). Due to the marked decline of deer hunters in much of northern Maine that invalidates use of this technique, we tested a double-count aerial survey method to estimate moose abundance in 9 northern WMDs. Density estimates ranged from 0.4–4.0 moose/km 2 , sightability was high (>70%) for all size moose groups (1–≥3 moose), and moose were well distributed across the landscape in early winter. The density estimates tracked closely with trends in moose sighting rate by moose hunters, harvest level, and hunter success rate in the survey area, and were consistent with jurisdictions in eastern Canada that also have low levels of predation and a preponderance of younger-aged forests. The double-count aerial survey is considered the preferred method to estimate population density, whereas hunter sighting indices would be most useful to track temporal population changes within a WMD.
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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 | 0.001 | 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".