Oxalates are Found in Many Different European and Asian Foods - Effects of Cooking and Processing
Bibliographic record
Abstract
Plant foods contain a surprising number of different toxins. A few well-known plants, including some grown in Thailand are known to contain high levels of oxalates however, some plants have not yet been fully investigated. A few plants have become fashionable to promote health because they contain antioxidants but some of these plants will contain oxalates as well. In many cases there is little published data to confirm the oxalates levels of these plants. If plant leaves are boiled before they are consumed this allows soluble oxalate to be leached out and discarded in the cooking water. This means that the cooked food contains considerably lower levels of soluble oxalates than the original raw plants. Cooking in a wok generally concentrates the oxalate contents as much of the cooking water is removed as steam. However, during cooking some of the soluble oxalates can combine with free calcium in the food and be converted to insoluble oxalates; these are not absorbed in the digestive tract. The preparation of juices using fruit or vegetables are being promoted as healthy alternatives, this poses further problems, as they may be prepared from raw vegetable leaves, such as spinach, which contain high levels of oxalates. These juices are not cooked so the oxalate concentration is not reduced during their preparation. Recent research has shown that the addition of calcium salts to these juices can considerably reduce the soluble oxalate content of the drink prepared without changing the taste.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".