Variation in frequency of CQA-tested municipal solid waste compost can alter metabolites in vegetables
Bibliographic record
Abstract
The use of compost to enhance plant growth and mineral nutrients composition are extensively studied but not much literature information exists on its influence on plant metabolic profiles. A study was performed to assess a 5-year variable frequency of application of Compost Quality Alliance tested municipal solid waste (MSW) compost effect on metabolic profiles of the edible portions of four different vegetable plants. The plants were lettuce (Latuca sativa cv. Grand Rapids), beets (Beta vulgaris cv. Detroit Supreme), carrot (Daucus carota cv. Nantes) and green beans (Phaseolus vulgaris cv. Golden Wax) grown under a sub-humid continental climate. The treatments were annual, biennial and no (control) applications of the MSW compost. Typically, soil fertility highly increased with the annual application of the MSW compost followed by the biennial application but declined in the control plot. The annually applied MSW compost increased total amino acids in the lettuce, carrot, beets, and green beans by ca. 323%, 109%, 94% and 18% respectively, compared to the control. Overall, total phospholipids were enhanced by the biennially applied MSW compost. Total organic acids in the lettuce, beets, and green beans were altered by the annual and biennial MSW compost applications by ca. 35% and 23%; 6% and 6.4%; and 22% and 65%, respectively compared to the control. A 2-dimension principal component analysis biplot confirmed positive association between the different frequencies of MSW compost application and soil fertility enhancement of plant metabolites. In conclusion, the annual application of MSW compost enhanced amino acids, phospholipids, acylcarnitines, amines and choline but reduced glucose in the lettuce, beets, carrot, and green beans. Further studies to elucidate the mechanisms underpinning such biofortification will be required.
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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) | 0.001 | 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".