Precision and accuracy of modal analysis methods for clastic deposits and rocks: A statistical and numerical modeling approach
Bibliographic record
Abstract
Quantifying the proportions of certain components in rocks and deposits (modal analysis or componentry) is important in earth sciences. Relevant methods for cross-sections (two- dimensional exposures) of clastic rocks include point counts or line counts. The accuracy of these methods has been supposed to be good in the literature but not necessarily verified empirically. Natural materials are inappropriate for assessing accuracy because the true proportions of each component are unknown. The precision of modal analysis methods has traditionally been evaluated from statistical models (primarily the normal approximation to the binomial distribution) but again rarely verified in practice because it is also extremely difficult to obtain different slices through the same material at outcrop scale. Here we create a set of numerical models of red and blue spheres with different proportions and sizes and cut 60 slices through the models, on which we perform point counts and line counts. We show that both of these methods are indeed able to retrieve the correct volumetric proportions of components, on average, when enough fragments are counted or intersected. As already known, precision is controlled by component abundance and the number of points counted or clasts intersected. However, we show that other important factors include differences between slices, which are relevant for our unequal-size models, and the proportion of voids, matrix, and/or cement in the rock. We present empirical precision charts for clast counts and line counts based on our models and make recommendations for future field studies.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".