Describing the geometrical packing of gravelly cobble deposits using pair-correlation functions
Bibliographic record
Abstract
There is a close correlation between the mechanical behavior of gravelly cobbles and their geometrical fabric. In geotechnical engineering, the particle-size distribution curve is used to describe the particle gradation. However, a group of particles with the same particle-size distribution can result in several packing arrangements due to the different sedimentation processes. The particle-size distribution curve does not distinguish this characteristic. This study attempts to employ the pair-correlation function of point field theory for describing the geometric packing of gravelly cobble deposits. In the point field, a single gravel- or cobble-sized particle is represented by a point of its geometric center. The pair-correlation function can statistically illustrate the characteristics of a geometrical point pattern and is helpful in interpreting the neighborhood relationship between particles, such as the frequency of interpoint distances and the dominant particle sizes in a point process. Some examples based on ideal particle shapes and arrangements are analyzed to illustrate the interpretations from the pair-correlation functions. The characteristics of pair-correlation functions of field examples are also explained. It is shown that the pair-correlation function can provide another approach to understanding the geometrical packing characteristics of a gravelly cobble formation.Key words: gravelly cobble deposit, geometrical packing, interpoint distance, particle-size distribution, pair-correlation function, point field theory.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".