Expectations of wildlife health surveillance systems and implications for system design
Bibliographic record
Abstract
Abstract Wildlife health surveillance is a rapidly evolving field. The goal of this commentary is to share the authors perspectives on the evolving expectations of wildlife health surveillance. We describe the basis for developing our opinions using multiple information sources including a narrative literature review, convenience samples of websites and conversations with experts. With increasing prominence of wildlife health, expectations for surveillance have increased. Situational awareness and threat or vulnerability detection were expected outputs. Action expectation themes included knowledge mobilization, reliable action thresholds and evidence-based decision making. Information expectations were broad and included the need for information on social and ecological risk drivers and impacts and evaluation of surveillance systems. Surveillance systems developers should consider: (1) What methods can equivalently and reliably manage the biases, uncertainties and ambiguities of wildlife health information; (2) How surveillance and intelligence systems support acceptable, ethical, efficient and effective actions that do not generate unintended consequences; and (3) How to generate evidence to show that surveillance and intelligence systems lead to decisions affecting vulnerability or resilience to endemic health threats, emerging diseases, climate change and other conservation threats.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".