The Influence of Processing Soil With a Coffee Grinder on Soil Classification
Bibliographic record
Abstract
Abstract : Use of a coffee grinder to break up clods of soils is one of the recommended practices of the Rapid Soils Analysis Kit (RSAK). This leads to the question that it may produce fines by cutting/breaking larger particles-potentially leading to the mis-classification of soil. In order to investigate this hypothesis, we performed a laboratory investigation to determine if the use of a coffee grinder produces fines from sand-size particles and whether the production of more finescan result in a different uses classification by some combination of adding soil fines and changing the Atterberg Limits of the fines tested. Three soils were tested--Ottawa Sand (SP), a poorly graded sand with silt (SP-SM) and a sandy lean clay (CL). Processing the soils for 90 seconds in a coffee grinder produced from 15.9% to 18.5% fines from the Ottawa Sand and established that the coffee grinder breaks down sand particles into fines. For the coarse Elevator Soil, originally an SP-SM, the fines production changed the soil classification to SM, while the Liquid Limit decreased from a measureable 21 to non-plastic, and Plastic Limit decreased from 19 to 17. For the Harte Clay (CL), the soil classification did not change; but the Liquid Limitand Plastic Limit both significantly increased. Therefore, for both the production of fines and the resulting impact on Atterberg Limits, the influence of using a coffee grinder to process soil cannot be quantified without further, systematic study.
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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 | 0.000 |
| 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".