Cautionary considerations for positive dingo management: a response to the Johnson and Ritchie critique of Fleming et al. (2012)
Bibliographic record
Abstract
Johnson and Ritchie (2012) have provided a criticism of our opinion piece (Fleming et al. 2012). There is some common ground, but we remain unconvinced by their view that our reasoning was unsound or beside the point. In this response, we discuss where Johnson and Ritchie have provided unconvincing evidence to refute our seven considerations, and reiterate and demonstrate why these considerations remain important. The mesopredator release or suppression hypothesis in Australian ecosystems must be objectively evaluated before positive management of dingoes and other free-ranging dogs is recommended or implemented. Adaptive comanagement of free-ranging dogs can be used for both biodiversity conservation and the mitigation of livestock predation but caution must be exercised when considering using free-ranging dogs as a conservation tool.
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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 | 0.068 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.060 | 0.080 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".