Relationship between recall of world's consumer products and China's consumer products
Bibliographic record
Abstract
In this paper,a model called Weight Regression and Forecasting among Econometrics is applied to evaluate the relationship between the number of recalled China's consumer products exported to EU,USA and Canada during 2005. 1 ~ 2012. 7 and the number of global recalled products in EU,USA and Canada. Result shows that a linear relationship between the two does exist with R-squared being 0. 9966. In this model,63% of the amount of monthly global consumer products recall is from the amount of monthly global made-in-china consumer products recall and 62. 2%,62. 4% and 63. 8% of the amount of monthly USA,EU and Canada consumer products are from made-in-china consumer products recall respectively. Our research indicates more than 60% of global consumer products recall, with inertia effect,is from China's consumer products recall,thus the safety of China's consumer products needs to improve continously.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".