The evaluation of macroscopic and microscopic textures of sand grains using elliptic Fourier and principal component analysis: Implications for the discrimination of sedimentary environments
Bibliographic record
Abstract
ABSTRACT A method that integrates elliptic Fourier and principal component analysis is a new development in the analysis of the shapes of sand grains. However, conventional elliptic Fourier and principal component analysis based on the variance–covariance matrix of the elliptic Fourier results can determine only the form of sand grains, and fails to quantify fine‐scale boundary smoothness of grains. In this study, sand grains from glacial, fluvial, foreshore and aeolian environments were analysed using both elliptic Fourier and principal component analysis and an extension of elliptic Fourier and principal component analysis based on the correlation matrix to extract information on grain form (macroscopic) and grain boundary smoothness (microscopic) separately. Conventional elliptic Fourier and principal component analysis based on the variance–covariance matrix produces macroscopic particle shape descriptors, such as the elongation index and bump indices. These indices indicate that sand grains exposed to subaqueous transportation (fluvial and foreshore) have forms that are more elongated than those exposed to subaerial transportation (aeolian dunes). However, elliptic Fourier and principal component analysis based on the correlation matrix is, in addition, able to extract microscopic particle features, which can be interpreted in terms of a boundary smoothness index. The boundary smoothness index indicates that the surfaces of glacial grains are the most rugged, whereas the surfaces of aeolian grains are the smoothest. On bivariate plots of the boundary smoothness and elongation indices, samples from fluvial, foreshore, aeolian and glacial environments cluster in discrete regions. In addition, the analysis reveals that glacial grains are exposed to different morphological maturation pathways than those from fluvial, foreshore and aeolian environments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".