An introduction to event history analyses for ecologists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Efforts to understand the emergence of an event require our ability to measure and understand the dynamics between time in a state (e.g., being alive or a behavior) and the outcome of the state. Studying the main drivers that affect changes in state over time allows researchers to better understand population dynamics and evolutionary processes. Event history analyses provide a range of theoretical and empirical tools to explore the emergence of an event. Their use is still restricted in ecology; however, they are commonly used in human demography. Event history analysis is a powerful tool for measuring the probability that an event occurs at time t . Here, we provide an introductory guide for ecologists who are interested in exploring event history analyses in their research. In the first part of this article, we outline key concepts in event history analyses and present a decision tree, statistical techniques, and their applications to ecological questions. To introduce practical applications of event history analyses, we provide four detailed tutorials, stemming from observational and longitudinal records of events in mammalian and avian species, along with relevant R scripts. We then explain how to interpret and present results of such analyses. Our results show that event history analyses are useful to quantify the effect of factors on the emergence of events. We conclude by highlighting additional strengths, pitfalls, and limitations researchers should be aware of when using such methods. We foresee the use of event history analyses for ecological studies.
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.
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 | 0.000 | 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) | 0.015 | 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 it