Reply to the discussion by Mesri and Wang on “Large-strain elastic viscoplastic consolidation analysis of very soft clay layers with vertical drains under preloading”
Bibliographic record
Abstract
The design of preloading involves loading schedule design and prediction of post-construction settlement. As the in situ measurement data are usually unavailable during the loading schedule design, it is difficult to estimate the preloading effects before construction. In this Reply, we first discuss the satisfied conditions of eq. (D4) in the Discussion (Mesri andWang 2015) based on Bjerrum’s creep diagram (Bjerrum 1967) and Yin–Graham’smodel (Yin and Graham 1989). Then the research objective and significance of the paper under discussion (Hu et al. 2014) are addressed. Based on Bjerrum’s creep diagram and Yin–Graham’s model, the void ratio at the end of service period for soft soil can be expressed as (Hu 2012)
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.037 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".