STATUS AND MANAGEMENT OF MOOSE IN THE PARKLAND AND GRASSLAND NATURAL REGIONS OF ALBERTA
Bibliographic record
Abstract
Moose (Alces alces) naturally colonized the Parkland Natural Region of Alberta during the 1980s and early 1990s, and later colonized the Grassland Natural Region by the early 2000s. We summarize population data during 1996–2016 for these regions, examining density, population trends, productivity, distribution, management, and moose-human conflicts to determine population status and sustainability. Within the Parkland, aerial surveys from one frequently monitored Wildlife Management Unit (WMU) indicated a significant increase (R2 = 0.7476, P < 0.001) in density, representing an annual rate of change of 1.07. Pooled data from an additional 21 Parkland WMUs indicated a mean annual rate of change of 1.11. Mean density for the 22 Parkland WMUs over the study period was 0.19 ± 0.06 moose/km2, and aerial surveys indicated a mean of 74.4 ± 3.6 calves/100 cows and 51.9 ± 2.9 bulls/100 cows. Within the Grassland, winter aerial survey data from 4 WMUs indicated a mean density of 0.05 ± 0.01 moose/km2, and 72.5 ± 6.75 calves/100 cows and 108.8 ± 34.4 bulls/100 cows. Hunting in these regions has been managed with a limited entry hunt. Resident rifle hunting opportunity for moose in the Parkland and Grassland increased 4.2-fold between 1996 and 2015. Opportunity in this region also represented an increasing proportion of that available province-wide, from 3.4% in 1996 to 19.8% in 2015.
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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 | 0.000 |
| 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 | 0.000 |
| 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".