Reply to the discussion by Tien H. Wu on "Slope stability thresholds for vegetated hillslopes: a composite model"
Bibliographic record
Abstract
In situ direct shear tests on soil with and without roots suggested that a composite soil–root system could consume energy while resisting shear displacement. To include this feature in a stability analysis of vegetated hillslopes we defined the safety factor in terms of energy associated with shearing in a soil–root system. We used this safety factor to determine the location of critical shear planes in our composite model, which was designed to estimate possible thresholds for vegetated hillslopes in the east coast of the North Island, New Zealand. After locating a critical shear plane, a simple infiltration model was used to find the time for the wetting front to reach the critical shear plane for known storm conditions. The discusser has raised some concerns regarding the validity of the proposed energy approach to analyze the stability of vegetated hillslopes. We believe the discusser has misinterpreted our data and reasoning. The discusser has not given valid evidence to support his point. In our response we use data presented in the discusser’s worked example to show the significance of the energy approach in estimating the stability of vegetated hillslopes.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.031 | 0.040 |
| Insufficient payload (model declined to judge) | 0.005 | 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".