The Relationship between Cake Strength of Potash Fertilizer and Initial Moisture Content, Particle Size, and Drying Time
Bibliographic record
Abstract
In this study, a new correlation for the cake strength of potash as a function of initial moisture content, particle size, and drying time is developed. The correlation is based on the experimental data collected by Wang et al. (2006 Wang , Y. , R. W. Besant , R. W. Evitts , & A. Dolovich . 2006 . Measurement of cake strength in potash using a centrifuge . Part. Part. Syst. Charact. 23 ( 5 ): 399 – 407 .[Crossref] , [Google Scholar]) and Gillies et al. (2006 Gillies , D. , Y. Wang , R. W. Evitts , & R. W. Besant . 2006 . The effect of particle size and magnesium content of the strength of caked potash . In Proceedings of the 5th International Conference for Conveying and Handling of Particulate Solids, Sorrento, Italy . Ch. 2, paper no. 206. (CD-ROM) . [Google Scholar]). For initial moisture content ranging from 0.25% to 6% (w/w), particle size ranging from 0.85 to 3.35 mm, and drying time constant ranging from 7 to 1900 min, the correlation shows that strength is linearly related to the product of the initial moisture content, X i , the inverse square of mean particle diameter, d pm , and , where t c is the drying time constant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".