Evaluating sources of censoring and truncation in telemetry‐based survival data
Bibliographic record
Abstract
ABSTRACT Bias in vital rate estimation may come from failing to meet a variety of assumptions during the stages of sampling, monitoring, and analysis, though most are not commonly addressed in published studies. Here, we pay specific attention to various forms of censoring and truncation that present challenges for telemetry‐based monitoring of survival. We use simulations to assess how uncertainty about times of death and imperfect detection probabilities affect Kaplan–Meier survival estimates. We then treat monitoring of threatened woodland caribou ( Rangifer tarandus caribou ) in west‐central Alberta as a case study to test for potential effects of non‐random right censoring and interval censoring on survival estimates. We report that monitoring frequency (e.g., daily vs. monthly) and associated uncertainty about the exact time of death do not inherently induce bias on Kaplan–Meier point estimates of survival nor affect estimates of variance. Removing individuals from the at‐risk pool during intervals for which they were not detected did induce a negative bias on resulting survival estimates when the probability of detection was independent of the animals' fates. We recommend using subsequent detections to impute animals' fates during missed intervals in such cases when all animals' fates are eventually known or permanently right censored and when missed detections are not related to changes in mortality risk. Although some assumptions remain difficult to test and of continued concern, we find no evidence of biases in the methodology of Alberta's woodland caribou monitoring program. Our results lend credence to recent evidence of widespread declines in woodland caribou populations across the province. © 2015 The Wildlife Society.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".