A Novel Framework to Protect Animal Data in a World of Ecosurveillance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Surveillance of animal movements using electronic tags (i.e., biotelemetry) has emerged as an essential tool for both basic and applied ecological research and monitoring. Advances in animal tracking are occurring simultaneously with changes to technology, in an evolving global scientific culture that increasingly promotes data sharing and transparency. However, there is a risk that misuse of biotelemetry data could increase the vulnerability of animals to human disturbance or exploitation. For the most part, telemetry data security is not a danger to animals or their ecosystems, but for some high-risk cases, as with species’ with high economic value or at-risk populations, available knowledge of their movements may promote active disturbance or worse, potential poaching. We suggest that when designing animal tracking studies it is incumbent on scientists to consider the vulnerability of their study animals to risks arising from the implementation of the proposed program, and to take preventative measures.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it