Perchlorate and Nitrate in Leafy Vegetables of North America
Bibliographic record
Abstract
In previous studies trace levels of perchlorate were found in lettuce (Lactuca sativa L.) irrigated with Colorado River water, which is contaminated with low levels of perchlorate from aerospace and defense related industries. In this paper, we report the results of a survey conducted across North America to evaluate the occurrence of perchlorate in leafy vegetables produced outside the lower Colorado River region, and evaluate the relative iodide uptake inhibition potential to perchlorate and nitrate in these leafy vegetables. Conventionally and organically produced lettuce and other leafy vegetable samples were collected from production fields and farmers' markets in the central and coastal valleys of California, New Mexico, Colorado, Michigan, Ohio, New York, Quebec, and New Jersey. Results show that 16% of the conventionally produced samples and 32% of the organically produced samples had quantifiable levels of perchlorate using ion chromatography. Estimated perchlorate exposure from organically produced leafy vegetables was approximately 2 times that of conventional produce, but generally less than 10% of the reference dose recommended by the National Academy of Sciences. Furthermore, the iodide uptake inhibition potential of perchlorate was less than 1% of that of the nitrate present. These data are consistent with those of other reported perchlorate survey work with lettuce, bottled water, breast milk, dairy milk, and human urine, and suggest a wide national presence of perchlorate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".