Comment Economics of a nest-box program for the conservation of an endangered species: a re-appraisal
Bibliographic record
Abstract
Spring et al. (D.A. Spring, M. Bevers, J.O.S. Kennedy, and D. Harley. 2001. Can. J. For. Res. 31: 19922003) recently published a paper on the economics of a nest-box program for the endangered arboreal marsupial, Leadbeater's possum (Gymnobelideus leadbeateri) in southeastern Australian forests. While their paper is a useful one, there are some important limitations of nest-box programs that need to be highlighted. In the case of Leadbeater's possum, we have undertaken extensive nest-box studies in Victoria mountain ash (Eucalyptus regnans F. Muell.) forests, where the vast majority of populations of the species now occur. Although large numbers of nest boxes have been deployed, very few have actually been occupied, which is a major problem since the effectiveness of any nest-box program will depend on patterns of use by the target species. Given very low levels of nest-box occupancy, harvesting regimes such as those that lead to on-site tree retention are needed to better conserve hollow-dependent species like Leadbeater's possum. Moreover, the need for nest boxes in the first place indicates that logging practices are presently not ecologically sustainable, and modified forestry practices need to be adopted.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 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".