A Simple Test Method for Rapid Measurement of Fines Content in Soils
Bibliographic record
Abstract
Abstract Visual soil classification methods used for estimating fines content are often relied upon in geotechnical investigations. The estimations of these methods are highly judgmental, generally erratic, and typically necessitate a confirmation. Laboratory mass-based wash tests are regularly performed on selected soil samples in order to verify or complement in situ visual classifications. Therefore, there is a dire need to improve the accuracy of fines content estimates of the visual methods. A preliminary study was conducted to assess the principle of estimating fines content by measuring relative volumes of the coarse-grained to fine-grained soil fractions. The results indicated soundness and adequacy of the principle. Utilizing this volume-based concept and the standard sample washing methods, a pilot study was conducted to develop and evaluate a more precise testing method, the mold test. Triplicate test runs were carried out on 144 soil samples. With run times of 5 to 15 min, the test is sufficiently rapid. The estimated fines contents of the samples were compared with that determined by the ASTM D1140 test. The absolute differences between the two estimates fell within ±5 % range, which is an appreciably higher accuracy than those of commonly used visual soil classification methods. Analysis performed on the results of the pilot study attested statistical competences of the proposed test method. This study has proven that the mold test is convenient for measuring fines content in soils at almost no cost—except minor consumables. The test method eliminates the subjectivity associated with current visual classification tests as well as the time and cost of the standard laboratory wash tests. While it is not intended to be a substitute for the latter, the mold test is an economically viable option that maintains balance between laboratory accuracy and practicality of the field methods.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".