Variability in Particle Size Distribution Due to Sampling
Bibliographic record
Abstract
Abstract Sampling particulate matter for particle size distribution (PSD) analysis is a task routinely performed in geotechnical and geoenvironmental engineering. Pitard (2019) mentions that “for a sample to be representative of anything, the first rule to fulfill is to ensure that the sample is representative of all the particle size fractions.” Several sampling techniques exist for obtaining samples of particulate matter from a lot, but their representativeness has rarely been documented, either experimentally or theoretically. To this end, this article studied the representativeness of four sampling techniques applied to moist and dry particulate matter, namely riffle splitting, fractional shoveling, 2-dimensional incremental sampling (2-DIS), and grab sampling. Bias being small because of experimental design, relative variance was used to assess sampling performance. Except for the largest size fraction (>9.5 mm), for which all sampling techniques gave poor results because of insufficient sample mass, riffle splitting was the most reproducible technique (CV = 6.47 %) and showed the smallest increase in variability compared to the fundamental relative sampling variance (i.e., a CV increase of 0.66 %), followed by fractional shoveling (7.68 %, 2.59 %), grab sampling (11.7 %, 6.51 %), and 2-DIS (16.3 %, 11.1 %). For fractional shoveling, sampling dry matter (CV = 19.2 %) significantly increased sampling variability compared to moist matter by 11.5 %. Furthermore, theoretical estimation of minimum sample mass requirements showed that mass requirements in ASTM D6913/D6913M-17, Standard Test Methods for Particle-Size Distribution (Gradation) of Soils Using Sieve Analysis, can lead to larger sampling variance than expected. Guidelines for specimen procurement ASTM D6913/D6913M-17 were also analyzed and judged insufficient with respect to fundamental sampling principles.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".