Comparing the results of two models in prediction of dysentery incidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective To ccompare the differences between winters multiplication model and autoregressive integrated moving average model(ARIMA) in predicting the incidence of dysentery in Beijing.Methods The monthly incidence data of dysentery from January 2007 to December 2012 in Beijing were collected and modeling the data with winters multiplication model and ARIMA.The results of predictingthe incidence of dysentery in the first quarter of 2013 in Beijing were evaluated.Results After the assessment of fit of these two models using data in 2012,measured by prediction percentage error,winters multiplication mode(l 1.13%)was found to be better than ARIMA(6.80%).The predicting incidence rates of dysentery by using the winters multiplication model in the first quarter of 2013 were 1.82/100000,1.54/100000 and 1.85/100000.Conclusions winters multiplication model could well reflect the trend of the incidence of dysentery in Beijing and it was suitable for predict ing the future trend dysentery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it