Complexity of factors affecting bobolink population dynamics communicated with directed acyclic graphs
Bibliographic record
Abstract
ABSTRACT North American grassland birds experienced steeper, more consistent, and widespread declines than other avian guilds in the past quarter century. Despite the surge of research into their ecology and conservation, there remains considerable uncertainty about the proximate and ultimate causes of declines and a need to clearly communicate its structural complexity among policy makers, resources managers, and stakeholders. We organized evidence from published literature about factors affecting population dynamics of bobolink ( Dolichonyx oryzivorus ), augmented by local stakeholder knowledge, in directed acyclic graphs (DAGs) depicting hypothesized cause–effect relationships. Our contrast between states of knowledge as depicted in the literature and among stakeholders revealed several factors and relationships not in evidence in the other. For example, stakeholders identified 3 secondary factors alleged to affect bobolink habitat quantity on breeding grounds not identified in the literature: tree planting incentives, green energy incentives, and crop values. These and other structural uncertainties in the management systems are made transparent using DAGs, facilitating communication and co‐learning among knowledge holders. Accounting for structural complexity and making it transparent at the outset of the planning and decision‐making process may reduce the probability of unanticipated outcomes as a result of poor management choices. © 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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".