Getting Information from Modal Decomposition of Grain Size Distribution Curves
Bibliographic record
Abstract
Abstract Soil samples may be difficult-to-identify mixtures of different layers. For environmental and groundwater projects, a detailed stratigraphy is needed because the coarse layers are highways for both water and dissolved contaminants. The paper proposes a method to decompose a grain size distribution curve (GSDC) into its 1, 2, or 3 log-normal components and their proportions. The proposed method can accurately decompose synthetic mixes of three lognormal modes, except when a mode contributes for less than about 2 %. In such a case, it is suggested to ignore this mode and describe the mix as a two mode mix. An example is given for an experimental site with many split-spoon soil samples. The suspected stratification was confirmed by the decomposition method, which found mixtures of only two soil components, fine sand and clayey silt, each of them with little variability. The large-scale permeability, as provided by a pumping test, corresponds to the horizontally composed permeability of the soil components: thus, it confirms the adequacy of the soil sample decomposition method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".