Inference in road ecology research: what we know versus what we think we know
Bibliographic record
Abstract
Roads and traffic impacts on wildlife populations are well documented. Three major mechanisms can cause them: reduced connectivity, increased mortality and reduced habitat quality. Researchers commonly recommend mitigation based on the mechanism they deem responsible. We reviewed the 2012-2016 literature to evaluate authors' inferences, to determine whether they explicitly acknowledge all possible mechanisms that are consistent with their results. We found 327 negative responses of wildlife to roads, from 307 studies. While most (84%) of these responses were consistent with multiple mechanisms, 60% of authors invoked a single mechanism. This indicates that many authors are over-confident in their inferences, and that the literature does not allow estimation of the relative importance of the mechanisms. We found preferences in authors' discussion of mechanisms. When all three mechanisms were consistent with the response measured, authors were 2.4 and 2.9 times as likely to infer reduced habitat quality compared to reduced connectivity or increased mortality, respectively. When both reduced connectivity and increased mortality were consistent with the response measured, authors were 5.2 times as likely to infer reduced connectivity compared to increased mortality. Given these results, road ecologists and managers are likely over-recommending mitigation for improving habitat quality and connectivity, and under-recommending measures to reduce road-kill.
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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 | 0.034 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".