mort: An R package to conservatively identify mortalities and shed tags in passive telemetry arrays
Bibliographic record
Abstract
Telemetry is commonly employed to track animals and support ecological inferences, yet the possibility that tagged animals have died or shed their tags is often not fully considered. Neglecting to consider mortality and/or tag shedding can lead to biases in the interpretation of results. We introduce the R package mort, developed to identify potential mortalities or shed tags in passive telemetry arrays. mort was designed for aquatic acoustic receivers with primarily non-overlapping detection radii, but the methods can be applied to any telemetry study that uses passive tracking (i.e. stationary receivers). We describe the primary functions and key options that are supported, provide guidance on the general use of the package, and demonstrate use with three case studies. The thresholds to identify mortalities are either user-defined or derived from the dataset itself (using observed durations of residences of animals that are known to be alive). This flexibility, along with numerous customizable options, allows application to multiple species and systems. mort fills an important gap in standardized workflows when processing and analysing passive telemetry data. The R package is a useful tool and will improve reproducibility in ecological research and management decisions that rely on results from passive telemetry.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.043 |
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".