The use of natural media amendments to produce kale enhanced with functional lipids in controlled environment production system
Bibliographic record
Abstract
Diets high in vegetable consumption is highly correlated with reduced risk of developing common lifestyle related diseases. We investigated the effects of three natural growth media amendments [potassium humate, dry vermicast, volcanic minerals or Promix alone (Control)] in enhancing the accumulation of functional lipids in greenhouse grown kale. Functional lipids (n9, n6, n3 fatty acids, diglycerides, galactolipids and phytosterols) were assessed using either gas chromatography/mass spectrometry (GC/MS) or ultra-high performance liquid chromatography-high resolution tandem mass spectrometry (UHPLC-HRMS/MS). The results showed volcanic minerals and dry vermicast were the most successful in enhancing the accumulation of functional lipids in kale. For example, dry vermicast enhanced the accumulation of total C18:1n9 and C16:3n3 fatty acids, while total C18:2n6 fatty acid accumulation was enhanced by volcanic minerals. In conclusion, natural growing medium amendments are remarkably effective in modulating the accumulation of functional lipids in kale grown under controlled-environment conditions. This could be a useful strategy for functional foods production in control environment production systems. Increase access to kale with enhanced functional lipids could aid in increase consumption of these health promotive compounds in the diet with potential implications in population health.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".