Extending Methods for Modeling Heterogeneity in Nest-survival Data Using Generalized Mixed Models
Why this work is in the frame
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Bibliographic record
Abstract
Strong interest in nest success has led to advancement in the analysis of nest-survival data.New approaches allow researchers greater flexibility in modeling nest-survival data and provide methods for relaxing assumptions and accounting for potentially important sources of variation.The most flexible method uses linear-logistic models with a random-effects framework to both incorporate potential covariate effects and model remaining heterogeneity.With the goal of increasing the use of more flexible methods, we provide additional detail regarding linear-logistic mixed models and their implementation.We use an example dataset to (1) demonstrate data preparation for analysis in PROC NLMIXED of SAS, (2) describe the use of code for evaluating competing models, (3) illustrate implementation of models with and without random effects and that evaluate potential effects of observer visits to nests, and (4) present methods of obtaining estimates of nest-survival rate for various covariate conditions of interest.We also conduct Monte Carlo simulations to evaluate the performance of linear-logistic mixed models of nest-survival data.We present the results of evaluation for one scenario and show that the estimation procedure as implemented in PROC NLMIXED is effective and that simulation can be used to gain insights into the advantages and disadvantages of various study designs.We encourage the development of further advancements that will allow greater flexibility in modeling.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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 it