Reply to the discussion by Ni et al. on “Analysis of the internal stability of granular soils using different methods”
Bibliographic record
Abstract
The authors wish to thank the discussers for their interesting comments that allow us to further improve the paper under discussion. The method proposed by the authors (Moraci et al. 2014) summarizes the commonly used criteria to evaluate the internal stability of cohesionless sand–gravel soils (i.e., Kezdi 1969; Sherard 1979; Kenney and Lau 1985, 1986; hereafter referred to as Kezdi, Sherard, and Kenney and Lau, respectively). The results of the method proposed by the authors are obtained as the average of Sherard’s and Kenney and Lau’s methods. Therefore, the authors’ method is conservative for soils for which both methods used provide conservative results. Kezdi’s criterion has not been considered in the reply because it is similar to but more conservative than Sherard’s.
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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 | 0.014 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.053 | 0.063 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".