Discussion of “Physical and numerical study of lateral diversion by three-layer inclined capillary barrier covers under humid climatic conditions”
Bibliographic record
Abstract
The authors investigated the performance of an inclined threelayer cover with capillary barrier effect (CCBE) system comprising silt, sand, and gravel, subjected to a continuous heavy rainfall (Zhan et al. 2014). They compared numerical results and analytical solutions with respect to effective length of lateral diversion of the inclined three-layer CCBE system. The comparison indicated that the analytical solution proposed by Stormont (1996) is relatively conservative. It should be noted that Stormont’s (1996) analytical solution is only applicable to a two-layer CCBE system. In the discussers’ opinion, this analytical solution should be modified to consider capillary effects contributed by both the upper two layers (i.e., silt–sand) and lower two layers (i.e., sand–gravel) in the three-layer CCBE system. The three-layer (silt—sand–gravel) CCBE system investigated by the authors can be simplified as the sum of silt–sand and sandgravel capillary barriers, as shown in Fig. D1. The infiltrated rainwater is first drained laterally along the silt–sand interface to the tip point at which the hydraulic conductivity of the sand layer is equal to that of the silt layer. Subsequently, rainwater flows into the sand layer and is drained laterally along the sand–gravel interface to the tip point at which the hydraulic conductivity of the gravel layer is equal to that of the sand layer. The lateral diversion length of the three-layer CCBE system, Lthree-layer, should be calculated as
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".