Effect of natural media amendments on crop quality under controlled environmental conditions
Bibliographic record
Abstract
The effect of different natural media amendments on the physical, chemical, and biological properties of soil is well studied and documented. Although there is more information on changes in soil parameters, few studies have demonstrated effects on crop quality. Herein, a greenhouse study was set up to identify the effect of wood ash (WA), paper sludge (SL), biochar (BC), and its combinations on kale’s nutritional profile grown in two different soil types (soil type 1, soil type 2) in Newfoundland, Canada. Pathway analysis revealed that although the same media amendments were used, the kale quality parameters were significantly altered by the grown soil type. It was shown that for soil type 2, most of the parameters were significantly enhanced compared to CTL (control) (p<0.05). For soil type 1, upon the addition of WA, SL, and BC there was a significant increase in the marketable yield; however, the crop’s nutritional parameters were not significantly changed. The lipidomics results showed that for soil type 1, WSBC (wood ash+sludge+biochar) amendment appeared to be effective in enhancing the functional lipids of 1,2 DGs, 1,3 DGs, ω6 lipids, and phospholipids. Whereas in kale grown in soil type 2, most of the studied functional lipids were increased upon the addition of SL and WBC (wood ash+biochar) media amendment. This different impact of media amendments on soils was also suggested from the pathway analysis revealing significantly different (p<0.05) lipid pathways associated with each soil type. In conclusion, WA, SL, and BC and the combination amendments showed a positive effect on kale’s nutritional profile, and this approach can be used to produce functional foods in controlled environmental conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".