Drying characteristics of forage sorghum stalks
Bibliographic record
Abstract
Forage sorghum has been identified as a potential source of biomass for heating by direct combustion. An important aspect of the production system is crop drying prior to storage or processing, such as pelletizing. In order to properly evaluate the effects of maceration or other similar treatments on the field drying of sorghum, a baseline of the drying characteristics of sorghum needs to be established. This was accomplished by drying samples of sorghum stalks in an Armfield UOP8 laboratory-scale tray dryer at an air temperature of 50°C and air velocity of 0.5 m/s. Stalks were cut into either 200 mm or 50 mm lengths. For some of the 200 mm lengths, the ends were sealed in paraffin wax to duplicate infinite cylinders so that the drying characteristics of the waxy skin could be determined separately from the cut ends. The data was fit to several standard exponential type models, including a two-term Newton model, and a separation of variables model based on Fick’s law. The length of the stalk and the sealing of the ends significantly affected the drying rate. Statistical indicators demonstrated that standard exponential models adequately capture the drying curves and are on par with the separation of variables model. The two-term Newton model and the separation of variables model provided a distinction between the axial and radial moisture migration. Using the separation of variables model, the effective diffusivity of sorghum stalks was determined to be 4.17 x 10-8 and 8.81 x 10-6 m2/hr in the radial and axial directions, respectively.
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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".