Making connections in aquatic ecosystems with acoustic telemetry monitoring
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
Autonomous acoustic telemetry monitoring systems have been deployed in aquatic ecosystems around the globe – from under ice sheets in the Arctic to coral reefs in Australia – to track animals. With tens of thousands of tagged aquatic animals from a range of taxa, vast amounts of data have been generated. As data accumulate, it is useful to reflect on how this information has advanced our understanding of aquatic animals and improved management and conservation. Here we identify knowledge gaps and discuss opportunities to advance aquatic animal science and management using acoustic telemetry monitoring. Current technological and analytical shortfalls still need to be addressed to fully realize the potential of acoustic monitoring. Future interdisciplinary research that relies on transmitter‐borne sensors and emphasizes hypothesis testing will amplify the benefits of this technology.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it