Amino Acids, Mineral Nutrients, and Efficacy of Vermicompost and Seafood and Municipal Solid Wastes Composts
Bibliographic record
Abstract
Growing medium amino acids and mineral nutrients stimulate rhizosphere activities and plant growth. A greenhouse experiment was performed to compare amino acid and mineral nutrient profiles of seafood waste compost (SFWC) and municipal solid waste compost (MSWC) and vermicompost (VC). Their efficacies were also tested on onion ( Allium cepa L. “Sweet Utah”). The control was Promix-BX™ alone. The MSWC, SFWC, and VC were composed of total of 36.4, 48.3, and 67.5 mg amino acids/100 g dry weight, respectively. Glutamic acid, aspartic acid, and glycine were the highest while methionine, histamine, and cysteine were the least in all the amendments. The VC had the highest Ca content but the least P and K contents. SFWC had the highest content of P and K and most of the determined micronutrients. The MSWC had significantly (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>P</mml:mi><mml:mo><</mml:mo><mml:mn fontstyle="italic">0.05</mml:mn></mml:math>) the highest N and leaf chlorophyll contents followed by the VC. The MSWC significantly (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>P</mml:mi><mml:mo><</mml:mo><mml:mn fontstyle="italic">0.05</mml:mn></mml:math>) increased anthocyanin content while the control recorded the least. The maximum quantum yield of photosystem II (Fv/Fm) and the potential photosynthetic capacity (Fv/Fo) were least in the VC treated plants. Dry matter was not affected by the type of amendment. Overall, plant growth was improved by the MSWC. Future research should investigate effect on secondary metabolites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".