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
Inference in generalized linear mixed models (GLMM) remains a topic of debate.\nBaayen, Davidson, and Bates (2008) outlines criticism against conventional ways of\nperforming inference for GLMMs. There are various alternatives proposed but lit-\ntle consistency is found on which is the most reasonable. Our focus is on assessing\ntemporal trends for mainly ecological count data. That is, we hope to provide a prag-\nmatic approach to Poisson GLMMs for ecological researchers within the statistical\nprogramming environment R. To achieve this, we start by providing a description of\nthe selected estimation and inferential procedures. We then complete a large scale\nsimulation to evaluate each of the estimation methods. We implement a power analy-\nsis to assess each of the selected inferential procedures. We then go on to apply these\nprocedures to data sampled by The National Parks of Canada. Finally, we conclude by giving a summary of our ?ndings and outlying work for the future.
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) | 0.000 | 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