83WILDLIFE AND CONSERVATION Researching wildlife in New Zealand:
Bibliographic record
Abstract
conservation applications are both constraints and opportunities face of practical research constraints and important ethical guidelines. A personal introduction As a new faculty member settling into my first job, at the University of Auckland, I was faced with many questions regarding the potential for basic scientific research on vertebrate animals in the uniquely New Zealand setting (Fraser & Hauber 2008). Before my arrival, I had been intensively studying a handful of bird species in North America, in particular the Brown-headed Cowbird (Molothrus ater) (Hauber et al. 2001), which as a species represented minor obstacles to obtain ethical and governmental approval for ver-tebrate-based studies of wild animals. Cowbirds, as native migratory birds, are themselves protected under USA and Canadian regulations. Yet, any research to capture, sample, manipulate, and take into captivity this species was quickly approved because cowbirds, as brood parasites laying their eggs into other birds’ nests, repress the reproductive success of their hosts (Hauber 2003), and so special exemptions are quickly granted so as to protect the many host species of this generalist social parasite. In contrast, studying native and endemic vertebrates in New Zealand requires not only a university animal ethic committee’s approval but also the combined for-mal support of local landowners, conservation agen-cies, and involved Iwi representatives. Specifically, the application to the New Zealand Department of Con-servation requires a special justification for why and how the proposed research benefits the species and its ecosystem with respect to conservation manage-ment. While certain aspects of basic biology, includ-ing animal behaviour and behavioural ecology, might
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".