Reply to the discussion by A. Eliadorani and Y.P. Vaid on "Effect of undrained creep on instability behaviour of loose sand"
Bibliographic record
Abstract
The discussers stated that the definition used in the note is “misleading.” According to the Oxford Advanced Learner’s Dictionary, the word “misleading” means “giving the wrong idea or impression and make them believe something that is not true.” The definition of instability is clearly defined and used consistently in the note. Therefore, we fail to see in which way it is “misleading.” If the discussers’ disagreement is on the term “undrained creep” or whether the instability should be called creep induced instability, we have explained in the note that the same term has been used and similar behaviour has been studied by Arulanandan et al. (1971) and Sheahan (1995) before for other types of soil. The discussers also stated “drained perturbation will not cause instability” and “there is only one instability line.” This may not be generally true, as it has been observed by a number of researchers (Eckersley 1990; Sasitharan et al. 1993; Chu et al. 2000, 2003b) that instability can occur under drained conditions and the instability line is not unique, but affected by many factors (Imam et al. 2002; Yang 2002). Creep is one of the factors and is studied in this note. The influence of void ratio on the instability line is also illustrated in Fig. 3 of the discussed note.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.027 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".