Strengthening links between waterfowl research and management
Bibliographic record
Abstract
ABSTRACT Waterfowl monitoring, research, regulation, and adaptive planning are leading the way in supporting science‐informed wildlife management. However, increasing societal demands on natural resources have created a greater need for adaptable and successful linkages between waterfowl science and management. We presented a special session at the 2016 North American Duck Symposium, Annapolis, Maryland, USA on the successes and challenges of linking research and management in waterfowl conservation, and we summarize those thoughts in this commentary. North American waterfowl management includes a diversity of actions including management of harvest and habitat. Decisions for waterfowl management are structured using decision analysis by incorporating stakeholder values into formal objectives, identifying research relevant to objectives, integrating scientific knowledge, and choosing an optimal strategy with respect to objectives. Recently, the consideration of the value of information has been proposed as a means to evaluate the utility of research designed to meet objectives. Despite these advances, the ability to conduct waterfowl research with direct management application may be increasingly difficult in research institutions for several reasons including reduced funding for applied research and the lower perceived value of applied versus theoretical research by some university academics. In addition, coordination between researchers and managers may be logistically constrained, and communication may be ineffective between the 2 groups. Strengthening these links would help develop stronger and more coordinated approaches for the conservation of waterfowl and the wetlands upon which they depend. © 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 | 0.081 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".