A common concentration-response function based on the results applying lags
Bibliographic record
Abstract
Introduction. Estimating the impact of short-term exposure on health outcomes needs knowledge of both the profile and magnitude of the relative risks. This motivates constructions of practical and reliable concentration-response functions (C-RFs). Aim. To define a practical method of finding concentration-response parametric function whose adjustable parameters can be tuned by data-driven well established routines. Material and methods. Mortality data for the period from 1987 to 2015 (10,592 consecutive days) in Montreal, Canada, are used for illustrative purposes. Exposure to ambient ozone measured by its concentration levels is considered health risk. Concentration-response function is built using statistical modelling, conditional Poisson regression, natural spline technique, and a rudimentary hierarchical data clustering. The case-crossover design is applied to fit the model of C-RF to the mortality data consisting of daily counts of non-accidental deaths. Results. Log-linear models of the concentration-response functions were computed for the concentrations and cofactors data lagged by 0 to 7 days; the results were statistically significant within this range of lags. The effectiveness of fitting was confirmed by reliable statistical tests. Digital routines were created to perform all computational tasks; software codes (written for R software platform) are included. The C-RF specifying the current responses to the cumulative exposure in several previous
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".