An integrated modeling approach for assessing management objectives for mule deer in central British Columbia
Bibliographic record
Abstract
ABSTRACT We used an integrated Bayesian state‐space population model to assess whether management objectives were met before (1995–2003), during (2004–2010), and after (2011–2013) antlerless permits to harvest mule deer ( Odocoileus hemionus ) were increased in response to stakeholder concerns in central British Columbia, Canada. Data inputs included 19 years of harvest data, 7 years of autumn age–sex composition data, 17 years of spring age–sex composition data, and 15 years of a population index. Management objectives were to maintain a spring population of 7,000–9,000 deer and a posthunt adult sex ratio of 20–30 males:100 females. An 8.5‐fold increase in antlerless permits raised the antlerless harvest rates from 1.0% (1995–2003) to 4.8% (2004–2010). Antlerless harvest rates decreased to 2.8% following a 53% decrease in permits from 2011 to 2013. Population projections from 2014 to 2018 fell within the bounds of the management objectives, but 95% credibility intervals revealed great uncertainty in population size and composition. We recommend a structured, adaptive approach to mule deer management that includes annual adjustment of harvests, monitoring, and modeling, with an open‐ended stakeholder engagement process to ensure objectives remain relevant, measurable, and achievable. © 2017 The Wildlife Society.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".