Acoustic and archival technologies join forces: A combination tag
Bibliographic record
Abstract
Abstract Technological advances are key to maximizing the information potential in electronic tagging studies. Acoustic tags inform on the location of tagged animals when they are in the range of an acoustic receiver, whereas archival tags render continuous time series of logged sensor measurements, from which trajectories can be inferred. We applied a newly developed acoustic data storage tag (ADST) on 154 animals of three fish species to investigate the potential of this combination tag. Fish trajectories were reconstructed from logged depth and temperature histories using an existing geolocation modelling approach, adapted to include a likelihood for acoustic detections. Out of 126 detected fish (accounting for over 700,000 detections) and 25 tag recoveries, eight ADSTs rendered both acoustic and archival data. These combined data could validate that the original geolocation model performed adequately in locating the fish trajectories in space. The acoustic data improved the timing of the daily position estimates. Acoustic and archival tagging technologies provided highly complementary information on fish movement patterns and could partly overcome the limitations of either technique. Furthermore, the ongoing developments to acoustically transmit summary statistics of logged data would further increase the information potential of combination tags when tracking aquatic species.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".