Statistical Modeling of Discrete Percentage Measurements With Application to Construction of Acceptance Bounds for Wood Failure in Structural Adhesive Testing
Bibliographic record
Abstract
Abstract The goals of this paper are: (1) to provide a statistical analysis approach that is appropriate for data from an interlaboratory study where responses are measured in discrete percentages and are subject to multiple sources of random variability, and (2) to apply this model to data on wood-failure percentages from block-shear tests on structural wood adhesives. We treat percentage responses measured in 5-point intervals as having arisen from observing 20 independent binary responses on different parts of the observed wood blocks. The overdispersion that is likely to result from the practical inadequacy of this assumption is overcome empirically by the inclusion of a random effect for blocks. We propose an analysis based on a parametric bootstrap to provide sampling distributions for statistics that regulators might wish to use in setting standards for acceptance of wood adhesives. Similar computational methods are developed to assess the fit of the model. This model is shown to provide a reasonably good fit for actual data in many of the cases to which it was applied.
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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 | 0.000 | 0.001 |
| 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".