Bibliographic record
Abstract
Reliable measurement of pressure within a particulate media has frustrated researchers in the field of soil mechanics and soil structure interaction for many years. The difficulty stems from the fact that most sensing systems involve compliance in the measurement concept. This compliance imparts a parasitic output to the measurement. In the past, researchers have attempted to calibrate this disturbance. Research has demonstrated that calibrations of this sort are dependent upon soil type, particle size, sensor geometry, and soil stiffness. Response of such sensors has been shown to be stress history–dependent: the response upon unloading may be highly nonlinear, resulting in a high degree of hysteresis. Complications in the measurement of soil pressure within a soil mass are significantly more complex from those met in the measurement of soil pressure at a structural boundary and must be considered separately. This paper utilizes a concept called the Null Soil Pressure System. Null pressure sensors were embedded and tested in a series of uniformly graded soils with particle diameters ranging from less than 0.15 to 15 mm. The data indicates that the response of the null pressure sensor consistently exceeds the actual soil pressure by a value of 4% ± 3%. This outcome is seen to be independent of soil type, particle size, stiffness, and stress history. No hysteresis is noted between loading and unloading, and additional loading cycles align with that of the virgin loading segment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".