Responses of natural <sup>15</sup>N abundance in cauliflower (<i>Brassica oleracea</i> L. var.<i> botrytis</i>) and soil to the application of organic and chemical fertilizers
Bibliographic record
Abstract
Natural nitrogen isotope abundance (δ15N) has been suggested as a potential indicator for discriminating organic vegetables from their conventional counterparts. However, little is known about the δ15N characteristic of cauliflower (Brassica oleracea L. var. botrytis) under organic production and how the application of chemical fertilizers could affect this characteristic. In this study, δ15N characteristics of cauliflower (including the intra-plant δ15N characteristic) and soil under different fertilization treatments were investigated under field conditions. Five treatments were laid out: control, chemical fertilizers (F4M0), organic fertilizer (F0M4), and chemical fertilizer in combination with organic fertilizer at the ratio of 3:1 (F3M1) and 1:3 (F1M3). Results showed that plant δ15N was positively correlated with fertilizer δ15N and plant root/flower was more sensitive to changes of fertilizer δ15N and soil δ15N than leaf/stem. Distribution of δ15N in different plant parts (especially in flower and leaf) was significantly changed when organic fertilizers were partially or totally substituted by chemical fertilizers, suggesting a comparison of flower δ15N and leaf δ15N might be applicable for the organic identification of marketed cauliflowers. Our results provide fundamental data for the soil- and fertilizer-specific crop δ15N database and indicate that the intra-plant δ15N characteristic could be helpful for organic cauliflower identification.
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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 | 0.000 | 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 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".