Reappraisal of Fall-Cone Flow Curve for Soil Plasticity Determinations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it