Bibliographic record
Abstract
Wildlife conservation and management occurs across the world through many different mechanisms and underlying principles. North America has developed a unique and successful process coined the North American Model of Wildlife Conservation. A key outcome of this model is that wildlife science informs management decisions, which are made by government officials in the public’s trust. If a species undergoes some form of legal take, managers are often required to ensure it is done responsibly with empirical evidence and consideration of ecological and societal objectives. Recent research suggests that 60% of wildlife management systems in Canada and the United States were not using science to guide their decisions, as they contained fewer than half of what they referred to as four “fundamental hallmarks of science”: measurable objectives, evidence, transparency, and independent review. We borrow from their framework and expand on it by evaluating whether white-tailed deer (Odocoileus virginianus) management in the northeastern United States includes the essential elements of a structured decision-making process. Our aim is to evaluate the regional management of a species that receives considerable focus to better understand whether the ideals of the North American Model of Wildlife Conservation are being implemented by way of a logical, transparent, and science-based decision-making process. Of the 11 states evaluated, seven had published a white-tailed deer management plan. Of these seven, we found that the “hallmarks” and most structured decision-making components were present, and the information collected was being used to inform decisions. Our findings indicate four main ways white-tailed deer management may be improved in the northeast United States: 1) states without a management plan should develop one, 2) states should incorporate an external review process, 3) states could consider alternative actions for each measurable objective and their consequences, and 4) states need to consider tradeoffs among multiple and possibly conflicting objectives. Our recommendations should lead to increased management transparency and build public support. Additionally, a key principle of The North America model of Wildlife Conservation is that science is the proper tool for discharging wildlife policy. Using science to understand population abundances and dynamics is especially critical in managing harvested wildlife. Tracking population changes allows resource managers to adapt regulations to ensure populations are maintained. In Rhode Island, USA white-tailed deer (Odocoileus virginianus) are annually harvested, but there is no systematic annual population estimation to track changes, which may put the population and forest ecosystem at risk. Our objective
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".