Isotopic characterization as a screening tool in authentication of organic produce commercially available in western North America
Bibliographic record
Abstract
The use of nitrogen stable isotopes to discriminate between conventionally and organically grown crops has been further developed in this study. Soil and irrigation water from different regions, as well as nitrogen fertilizers used, have been examined in detail to determine their effects on nitrogen isotope composition of spinach, lettuce, broccoli and tomatoes. Over 1000 samples of various types of organically and conventionally grown produce of known origin, along with the samples of nitrogen fertilizers used for their growth, have been analysed in order to assemble the datasets of crop/fertilizer correlations. The results demonstrate that the developed approach can be used as a valuable component in the verification of agricultural practices for more than 25 different types of commercially grown green produce, either organic or conventional. Over a period of two years, various organic and non-organic greens, from different stores in Seattle (WA, USA) and Victoria (BC, Canada), were collected and analysed using this methodology with the objective of determining any pattern of misrepresentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".