The Study of Post-Harvest Processing and Handling of Residues From Plants Grown Primarily For Agronomics: Soybean Stalk, Corn Stover, Tomato Vine, Cucumber, Eggplant, and Summer Squash
Bibliographic record
Abstract
Information on post-harvest handling of the crop is critical to the development of new or improved plant species and traits. This paper presents a comprehensive study of the grinding characteristics and handling properties of a number of crop residues under agronomic studies. We used a laboratory-scale knife mill connected to an in-line power meter to investigate the specific energy of size reduction for each crop. The summer squash sample yielded the smallest mean particle size upon grinding (P= 0.05). The results indicate a significant correlation between the Carbon to Oxygen (C/O) ratio and the Gross Calorific Value (GCV), ash, lignin content, and net specific grinding energy consumption (NSGEC) of the samples. Among agricultural residues, the soybean stalk sample, with the highest C/O ratio (0.96), exhibited the highest GCV (17.5 MJ/kg, db) and NSGEC (31.7 kWh/t), while the summer squash sample, with the lowest C/O ratio (0.46), showed the lowest GCV (13.6 MJ/kg, db) and NSGEC (5.1 kWh/t). The flowability of the ground biomass samples varied, with cucumber showing the best free flow properties. The results also showed that there is a significant positive correlation between the lignin content and NSGEC of all samples (p= 0.05).
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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".