Characteristics of the Macronutrient Content of Compost and Liquid Organic Fertilizer from Agricultural Wastes
Bibliographic record
Abstract
Agricultural products have great potential to produce untapped farm-to-table agricultural waste. This can happen because the agricultural products are damaged before they reach consumers and become agricultural waste. Therefore, paper aims to investigate the macronutrient content of the compost and liquid organic fertilizer using agricultural waste as the main ingredient. There are two treatments for making compost, namely the composition of the main ingredients (rice straw + lamtoro, rice straw + corn stalks, and lamtoro + corn stalks) and the composition of the supporting material in the form of livestock manure (cow dung, goat manure, and chicken manure). There are five treatment sources of the main raw materials for making liquid organic fertilizer tested, namely banana peel, papaya peel, pineapple skin, tomato, and cassava peel. Macro parameters in the form of N, P, K, and C content were measured using the Kjeldahl, Bray, AAS, and Walkle and Black methods, respectively. The C/N ratio was calculated by comparing the content of C and N. Furthermore, data were analyzed using statistical parameters in the form of ANOVA and DMRT. Making compost with the main ingredients of agricultural waste and supporting materials from livestock manure has a significant effect on macronutrient content in compost. Apart from that, the production of liquid organic fertilizer with the main ingredient of agricultural waste significantly affects the macronutrient content of liquid organic fertilizer produced.
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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".