Electronic tracking tag programming is critical to data collection for behavioral time-series analysis
Bibliographic record
Abstract
Electronic tracking tags are major tools of ecological research and management, but programming sophisticated tags can be challenging. We discovered that a common programming scheme can negatively affect the quality of tracks collected by Argos tags. Here we describe the problem and how it occurred. We then simulated a series of tracks with different data collection schemes to investigate how spatial precision and temporal frequency affect the overall quality of tracking data. Tracks were simulated using a two-state composite correlated random walk (CCRW). Tracks were simulated with two spatial scales, using parameters estimated from northern elephant seal (large scale) and California sea lion (small scale) tracking data. Onto each simulated track, observations of varying precision, frequency, and censoring were imposed. We then fit the CCRW in a state-space model (SSM) to the simulated observations in order to assess how data quality and frequency affected recovery of known behavioral state and location. We show that when movement scales are small, regular observations were critical to recover behavior and location. In addition, tracks with frequent regular locations (increasing N) overcame low spatial accuracy (e.g., Argos) to detect small-scale movement patterns, suggesting frequently collected Argos locations may be as good as infrequently collected GPS in some circumstances. From these results and our experience tracking animals generally, we produce a set of guidelines for those manufacturing, programming, and deploying electronic tracking tags to maximize the utility of the data they produce.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".