Is it even rainier in North Vancouver? A non-parametric rank-based test\n for semicontinuous longitudinal data
Bibliographic record
Abstract
When the outcome of interest is semicontinuous and collected longitudinally,\nefficient testing can be difficult. Daily rainfall data is an excellent example\nwhich we use to illustrate the various challenges. Even under the simplest\nscenario, the popular 'two-part model', which uses correlated random-effects to\naccount for both the semicontinuous and longitudinal characteristics of the\ndata, often requires prohibitively intensive numerical integration and\ndifficult interpretation. Reducing data to binary (truncating continuous\npositive values to equal one), while relatively straightforward, leads to a\npotentially substantial loss in power. We propose an alternative: using a\nnon-parametric rank test recently proposed for joint longitudinal survival\ndata. We investigate the benefits of such a test for the analysis of\nsemicontinuous longitudinal data with regards to power and computational\nfeasibility.\n
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".