State‐space framework for estimating measurement error from double‐tagging telemetry experiments
Bibliographic record
Abstract
Summary 1. Double‐tagging experiments are invaluable for determining the accuracy and precision of location data provided by different telemetry technologies used with free‐ranging animals. 2. We developed a state‐space modelling framework for estimating the precision of telemetry location data based on double‐tagging experiments. The model is simultaneously fitted to multiple data types with different temporal resolutions while including errors in all data. 3. We used the model to estimate the precision of a specific geolocation method based on light and sea surface temperature applied to a large marine telemetry dataset. Data were available from double‐tagging experiments on 111 animals representing seven marine species including 4 sharks, 2 birds and 1 pinniped. Study animals carried electronic tags that provided geolocation estimates as well as more precise satellite‐based location data (Argos and Global Positioning System). 4. Estimates of the precision of geolocations were similar to previous findings. The overall estimated SD of geolocation errors for each species ranged from 0·5 to 3·9° for longitude and 0·8 to 3·6° for latitude. 5. While these results are specific to this particular type of location estimation method, the state‐space framework presented here is a robust approach to estimating the precision of various types of telemetry location data from double‐tagging experiments. The model simultaneously allows for appropriate inferences about true animal locations and movement.
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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".