Bibliographic record
Abstract
The objective of this research was to develop a new method for in- bin grain porosity measurement without sampling.The new method was based on the gas volume- pressure relationship.Based on the amount of air pumped into the bin to fill the pore space and the corresponding pressure rise,the total pore volume was calculated.An experiment was conducted to validate this method.The experimental set up was made up of a model bin,with an air inlet controlled by a ball valve,an air pump,a duct,a hot wire anemometer,a thermometer,and a pressure gauge.Before the experiment,all connections were checked thoroughly to ensure the system was air- tight.As the air was pumped to the bin,the pressure increased from the atmospheric pressure to a maximum value.This maximum pressure,as well as the time took to reach this pressure were recorded.The flow rate measured by the anemometer was recorded continuously by using a video camera.Seventy- five( 75) tests were performed on wheat and corn.To validate the proposed methods,the porosity of wheat and corn was also measured with the commonly used liquid displacement method.The average measured porosities for the wheat and corn were 34.5% and 41.6%,respectively.And the relative error between the in- bin measurement method and reference value gained by the liquid method( wheat: 33.0%,corn: 40.0%) were 4.55%and 4.00%,respectively.This showed that the proposed in- bin porosity measurement method was adequate and could be used to measure the porosity of grain as a new approach.The advantage of this method is that it could measure the grain porosity during storage without taking samples,and it reflects the true porosity in the bin.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".