Bibliographic record
Abstract
Abstract Penalty splines such as smoothing spline and P‐spline, as well as unpenalized regression splines, have become increasingly popular methods in contemporary non‐parametric and semiparametric regressions, particularly for data arising from longitudinal, multilevel, and spatiotemporal settings. In the recent decade, the development of the Markov chain Monte Carlo (MCMC) methods has facilitated applications of flexible spline fittings via Bayesian hierarchical formulation. In this paper, we study three spline smoothing methods in the context of spatiotemporal modeling of rates and Bayesian disease mapping. We explore their potentials for fully Bayesian (FB) rate and risk trends ensemble estimation and spatiotemporal relative risks inference. In particular, our study presents and compares Bayesian hierarchical formulations of regression B‐spline, smoothing spline, and P‐spline, explores the connections and distinctions among them, and sheds light on their varying capabilities as ‘data‐driven’ smoothers for risk trends exploration and sequential disease mapping. The methods are motivated and illustrated through a Bayesian analysis of adverse medical events (commonly known as iatrogenic injures) to hospitalized children and youth in British Columbia, Canada. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".