Bibliographic record
Abstract
Investment in wild animal health has not kept pace with investment in health programs for agriculture or people. Previous arguments of the inherent value of wildlife or the possible public health or economic consequences of fish or terrestrial wildlife diseases have failed to motivate sufficient, sustained funding. Wildlife health programs are often funded on an issue-by-issue basis, most often in response to diseases that have already emerged, rather than being funded to protect and promote the health of wild animals on an ongoing basis. We propose that one explanation for this situation is the lack of business cases that explains the value of wild animal health programs to funders. This paper proposes a set of building blocks that inform the creation of wildlife health business cases. The building blocks are a series of questions derived from a literature review, the experience of directors of two large national wildlife health programs and lessons learned in developing a draft business case for one of those programs. The six building blocks are: (1) Know what you are trying to achieve; (2) Describe your capabilities; (3) Identify factors critical to your success; (4) Describe the value you can bring to supporters; (5) Identify who needs your services and why; and (6) Share the plan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".