Contamination of Bottled Waters with Antimony Leaching from Polyethylene Terephthalate (PET) Increases upon Storage
Bibliographic record
Abstract
Antimony concentrations were determined in 132 brands of bottled water from 28 countries. Two of the brands were at or above the maximum allowable Sb concentration for drinking water in Japan (2 microg/L). Elevated concentrations of Sb in bottled waters are due mainly to the Sb2O3 used as the catalyst in the manufacture of polyethylene terephthalate (PET(E)). The leaching of Sb from PET(E) bottles shows variable reactivity. In 14 brands of bottled water from Canada, Sb concentrations increased on average 19% during 6 months storage at room temperature, but 48 brands of water from 11 European countries increased on average 90% under identical conditions. A mineral water from France in PET(E), purchased in Germany, yielded 725 ng/L when first tested, but 1510 ng/L when it was stored for 6 months at room temperature; the same brand of water, purchased in Hong Kong, yielded 1990 ng/L Sb. Pristine groundwater containing 1.7+/-0.4 ng/L Sb (n = 6) yielded 26.6+/-2.3 ng/L Sb (n = 3) after storage in PET(E) bottles from Canada for 6 months versus 281+/-38 ng/L Sb (n = 3) in PET(E) bottles from Germany. Tap water bottled commercially in PET(E) in December 2005 contained 450+/-56 ng/L Sb (n = 3) versus 70.3+/-0.3 ng/L Sb (n = 3) when sampled from a household faucet in the same village (Bammental, Germany), and 25.7+/-1.5 ng/L Sb (n = 3) from a local artesian flow.
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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 | 0.001 |
| 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 | 0.001 | 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".