An alternative method for determining particle-size distribution of forest road aggregate and soil with large-sized particles
Bibliographic record
Abstract
Measurement of particle-size distribution (PSD) of soil with large-sized particles (e.g., 25.4 mm diameter) requires a large sample and numerous particle-size analyses (PSAs). A new method is needed that would reduce time, effort, and cost for PSAs of the soil and aggregate material with large-sized particles. We evaluated a nested method for sampling and PSA by comparing it with the methods that follow the American Association of State Highway and Transportation Officials (AASHTO) standard T88-00 and the American Society for Testing and Materials (ASTM) standard D422-63. Using 33 forest road aggregate samples from the Clearwater National Forest in northern Idaho, the nested method required much less laboratory time and effort and resulted in similar PSA values, except for the 0.149 mm (No. 100) sieve, where the smallest particle-size fraction (PSF) values (1.30% and 1.39%) were observed. The nested method shows great potential for determining PSDs of the soil and aggregate material with large-sized particles and should be tested on other forest soils and road aggregates. The nested sampling and analysis method allows for the same number of samples to be collected but requires less laboratory time, making it more efficient and economical for testing the soil and aggregate material with large-sized particles such as forest road aggregate.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".