The Influence on Soil Classification of Processing Soil With a Coffee Grinder
Bibliographic record
Abstract
Abstract The rapid soils analysis kit uses a coffee grinder to break apart aggregated soil particles in fine-grained soils. This leads to the question of potential particle cutting or breaking due to the grinder. Hence, we performed a laboratory investigation to determine if the use of a coffee grinder to break apart aggregated soil particles produces fines from cutting or breaking sand-size and smaller particles and whether the production of more fines sometimes changes the USCS classification by some combination of adding soil fines and altering the Atterberg limits. Processing Ottawa Sand for 90 s in a coffee grinder established that the coffee grinder breaks down sand particles into fines. A silty sand (SM) sand was tested in the same way and the fines content increased significantly. Several tests were performed on fine-grained soils, and no significant increase in fines due to processing in the coffee grinder was noted. The Atterberg limits of all soils tested changed little due to processing with the grinder. In particular, the plastic limit was not changed by more than 2 (% water content). Several recommendations were made for potential future investigations.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".