Chemical content of unrecorded distilled alcohol (bai jiu) from rural central China: Analysis and public health risk
Bibliographic record
Abstract
Newman, I., Qian, L., Tamrakar, N., Feng, Y., & Xu, G. (2017). Chemical content of unrecorded distilled alcohol (bai jiu) from rural central China: Analysis and public health risk. The International Journal Of Alcohol And Drug Research, 6(1), 59-67. doi:http://dx.doi.org/10.7895/ijadr.v6i1.236Aims: To test 47 samples of locally distilled unrecorded beverage alcohol (bai jiu) obtained in rural central China.Methods: Alcohol samples purchased from home-based makers or from small village shops were analyzed for ethanol, methanol, acetaldehyde, ethyl acetate, six higher alcohols, arsenic, cadmium, and lead. Results were judged against the standards for these compounds set by the AMPHORA Project.Findings: Ethanol concentrations ranged from 38.7% to 63.7% (mean 50.4%). Methanol and methyl acetate detected in all samples did not exceed the Alcohol Measures for Public Health Research Alliance (AMPHORA) limits. Acetaldehyde was present in all samples, with three samples exceeding the AMPHORA limit by a small amount. Lead was found in 57.4% of the samples with one sample exceeding the AMPHORA limit; cadmium was found in 89.4% of the samples with two exceeding the AMPHORA limit. Arsenic was found in 46.8% of the samples with none exceeding the AMPHORA limit.Conclusions: The three samples that exceeded AMPHORA limits for cadmium or lead are of concern in terms of the potential of long-term exposure for local people who regularly consume locally made bai jiu. The main health concern from bai jiu appears to be the risk associated with high ethanol concentration—the same health concern as for recorded, commercially produced spirits.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".