Reappraisal of Fall-Cone Flow Curve for Soil Plasticity Determinations
Bibliographic record
Abstract
Abstract Several attempts have been made to devise alternate plastic limit (PL) determination methods, targeting higher degrees of repeatability and reproducibility. Among these, empirical-type correlations linking the plasticity index (PI) to the flow index (FI)—the slope magnitude of the flow curve—seem to be gaining increased attention, particularly for the fall-cone (FC) approach, and hence demand further examination. To better understand the true potentials and limitations of this emerging practice for soil plasticity determination, this study presents a critical statistical appraisal of FI-based correlations—using a large and diverse database of 230 FC tests (for the 80 g–30° cone)—in estimating the PI (and hence the PL). It is demonstrated that the so-called “strong” correlation between the PI and FI reported in some literature, favoring the use of FI as a PI estimator, is an overlooked “statistical pitfall” originating from an over-reliance on the coefficient of determination (R2) statistic. Employing appropriate error-related statistics, it is shown that the PI predictions made by FI-based correlations are associated with high average errors of 22–33 %. Hence, such correlations, at best, can only provide a rough approximation of the actual PI (and hence PL). An attempt is also made to assess the validity of FI-based correlations in the context of soil classification using the Casagrande-style plasticity chart. The agreement level between the conventional classification approach and that performed using PI deduced from FI-based correlations was 75–80 %. This analysis, however, did not account for errors in the rolling-thread plastic limit data, maintaining a strong possibility that FI-based correlations may be suitable for routine soil classification purposes.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".