Bulk Solids Handling Properties of milled, 2mm and 4 mm corn stover with and without MgSt
Bibliographic record
Abstract
Data from FT-4 shear testing and some analysis code The analysis code is based on the mass flow hopper calculations and testing described by Andrew W Jenike. Storage and Flow of Solids Bulletin No. 123. Bulletin of the UtahEngineering Experiment STation, 53(26):209, November 1964. doi: 10.2172/5240257. Dietmar Schulze. Powders and Bulk Solids. Springer Berlin, Heidelberg, Berlin, Heidel-berg, 1 edition, September 2007. ISBN 978-3-540-73767-4. Mehos, G. STORAGE AND HANDLING OF BULK SOLIDS.https://mehos.net/downloads, 2023; (accessed 2024-01-05). These references are also used for specific equations in the example code: Andrew W Jenike and J.R. Johanson. Review of the Principles of Flow of Bulk Solids.Canadian Institute of Mining, Metallurgy and Petroleum, 71:141–146, 1970. P. C. Arnold and A. G. McLean. An analytical solution for the stress function at thewall of a converging channel. Powder Technology, 13(2):255–260, March 1976. ISSN0032-5910. doi: 10.1016/0032-5910(76)85011-5. Greg Mehos, Mike Eggleston, Shawn Grenier, Christopher Malanga, Grishma Shrestha,and Tristan Trautman. Designing hoppers, bins, and silos for reliable flow. The Best ofEquipment Series, 33, 2018. Z. H. Gu, P. C. Arnold, and A. G. McLean. Modelling of air pressure distributions inmass flow bins. Powder Technology, 72(2):121–130, October 1992. ISSN 0032-5910. doi:10.1016/0032-5910(92)88018-D. D18 Committee. Test Method for Shear Testing of Powders Using the Freeman TechnologyFT4 Powder Rheometer Shear Cell. URL https://www.astm.org/d7891-15.html.(accesseed 2024-06-01). Greg Mehos. Maximum solids discharge rates from hoppers. Chemical EngineeringResearch and Design, 191:564–567, March 2023. ISSN 0263-8762. doi: 10.1016/j.cherd.2023.01.050.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".