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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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".