Evaluation of the Reduction of Tsunami Damages Based on Local Wisdom Contermeasures in Indonesia
Bibliographic record
Abstract
Local wisdoms such as a traditional ethics, land-use system and others had sometimes mitigated tsunami damages in Indonesia. The effective use of those local wisdoms is strongly desired especially in developing countries, because it is quite difficult for those countries to allocate enough budgets for constructing hard type of countermeasures against tsunami. Among local wisdoms against tsunami hazard, this study evaluates the efficiency of a hollow topography which can be seen on the beach along Lampon village in Indonesia. Artificial hollows are arrayed on the beach as one of the local wisdoms in Lampon village to reduce the intensity of inundated tsunami flow. The numerical simulation of tsunami inundation is conducted to evaluate the efficiency of this hollow topography. Furthermore, this study evaluates the efficiency of some contrivances, such a combination of vegetation area and a multiple-use of hollow and embankment topography, in order to enhance the performance of countermeasure based on the local wisdom.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".