3D Pore Structure Characterization of Stored Grain Bed
Bibliographic record
Abstract
Highlights An image analysis for reconstruction of 3D pore structure within bulk grain was presented. Mathematical models for porosity and tortuosity were developed from the 3D reconstructed images. The mathematical models can be incorporated in computational model of flow through bulk grains. Abstract. An image analysis technique for reconstruction of the complex 3D pore structure within bulk grain from 2D section images was presented. The technique relies on aligning successive 2D images of cut-sections obtained from colored-wax solidified soybean grain beds, which were then subjected to image processing using ImageJ software developed by the National Institute of Health (NIH, Bethesda, Md.) for the reconstruction and visualization of different airflow paths within the bulk grain. Porosity and tortuosity values were quantified from the 3D image volume and 3D reconstructed inter-connected airflow paths to develop empirical mathematical models for predicting porosity and tortuosity as a function of compaction due to the pressure exerted by the grain depth. Results indicated that the rate of decrease in porosity was higher at the lower compaction grain depth and then gradually approached a minimum value as the compaction grain depth increased. At the top of the compacted grain, the porosity of the tested soybean bed was determined to be 0.42 and reduced to 0.34 at a compaction pressure of 14.2 kPa (equivalent to a compaction grain depth of 25 m). Tortuosity increased with the compaction pressure from 1.15 to 1.58 at a compaction pressure of 14.2 kPa (equivalent to 25 m of grain depth), or by 37.4%. Keywords: Grain bed, Image analysis, Pore structure, Porosity, Tortuosity.
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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".