PHYSICAL PROPERTIES OF CORN RESIDUES
Bibliographic record
Abstract
Corn residues (cobs, leaves and stalks) are abundantly available renewable materials that can be used as an energy source in gasification and combustion systems.Proper understanding of the physical properties of these materials is necessary for their use in thermochemical conversion processes.The physical properties (moisture content, particle size, bulk density and porosity) of corn cobs, leaves and stalks were determined in this study.The moisture contents were 6.38, 7.92 and 6.40% of the cobs, leaves and stalks, respectively.The cobs had the highest weight percentage (18.23%) of the small particles (<0.212 mm) and the leaves had the highest weight percentage (40.10%) of large particles (>0.850 mm).Most of the stalk particles (84.82%) were in the range of 0.212-0.850mm.The cob particle size had a normal concave (inward) distribution between particle sizes 0.106 mm (18.23 weight %) and 0.925 mm (25.26 weight %) with the lowest weight percentage (5.30weight %) at 0.390 mm particle size while the stalk particle size had a normal convex (outward) distribution between particle sizes 0.106 mm (8.49weight %) and 0.925 mm (6.69 weight %) with the highest weight percentage (23.47weight %) at the 0.605 mm particle size.The leaves had an increasing trend of particle size distribution between the particle sizes 0.106 and 0.925 mm.The average particle sizes for the cobs, leaves and stalks were 0.56, 0.70 and 0.49 mm, respectively.The average bulk density was 282.38, 81.61 and 127.32 kg m -3 for the corn cobs, leaves and stalks, respectively.The average porosity was 67.93, 86.06 and 58.51% for the corn cobs, leaves and stalks, respectively.A positive relationship between the average particle size and the porosity was observed for the corn residues.The differences in the physical properties among the corn residues (cobs, leaves and stalks) observed in this study are due to variations in the compositions and structures of these materials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".