Generalized additive models to capture the death rates in Canada\n COVID-19
Bibliographic record
Abstract
To capture the death rates and strong weekly, biweekly and probably monthly\npatterns in the Canada COVID-19, we utilize the generalized additive models in\nthe absence of direct statistically based measurement of infection rates. By\nexamining the death rates of Canada in general and Quebec, Ontario and Alberta\nin particular, one can easily figured out that there are substantial\noverdispersion relative to the Poisson so that the negative binomial\ndistribution is an appropriate choice for the analysis. Generalized additive\nmodels (GAMs) are one of the main modeling tools for data analysis. GAMs can\nefficiently combine different types of fixed, random and smooth terms in the\nlinear predictor of a regression model to account for different types of\neffects. GAMs are a semi-parametric extension of the generalized linear models\n(GLMs), used often for the case when there is no a priori reason for choosing a\nparticular response function such as linear, quadratic, etc. and need the data\nto 'speak for themselves'. GAMs do this via the smoothing functions and take\neach predictor variable in the model and separate it into sections delimited by\n'knots', and then fit polynomial functions to each section separately, with the\nconstraint that there are no links at the knots - second derivatives of the\nseparate functions are equal at the knots.\n
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".