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.
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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 | 0.236 | 0.592 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".