Carbon and Nitrogen Isotopic Signatures and Nitrogen Profile To Identify Adulteration in Organic Fertilizers
Bibliographic record
Abstract
Recently it has been shown that stable isotopes of nitrogen can be used to discriminate between organic and synthetic fertilizers, but the robustness of the approach is questionable. This work developed a comprehensive method that is far more robust in identifying an adulteration of organic nitrogen fertilizers. Organic fertilizers of various types (manures, composts, blood meal, bone meal, fish meal, products of poultry and plant productions, molasses and seaweed based, and others) available on the North American market were analyzed to reveal the most sensitive criteria as well as their quantitative ranges, which can be used in their authentication. Organic nitrogen fertilizers of known origins with a wide δ(15)N range between -0.55 and 28.85‰ (n = 1258) were characterized for C and N content, δ(13)C, δ(15)N, viscosity, pH, and nitrogen profile (urea, ammonia, organic N, water insoluble N, and NO3). A statistically significant data set of characterized unique organic nitrogen fertilizers (n = 335) of various known origins has been assembled. Deliberately adulterated samples of different types of organic fertilizers mixed with synthetic fertilizers at a wide range of proportions have been used to develop the quantitative critical characteristics of organic fertilizers as the key indicators of their adulteration. Statistical analysis based on the discriminant functions of the quantitative critical characteristics of organic nitrogen fertilizers from 14 different source materials revealed a very high average rate of correct classification. The developed methodology has been successfully used as a source identification tool for numerous commercial nitrogen fertilizers available on the North American market.
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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 | 0.001 | 0.001 |
| 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 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".