Analysis of Landslide Vulnerability in Agribusiness Development Efforts Environmental Insight in Ngargoyoso District
Bibliographic record
Abstract
<p><em>The area of Ngargoyoso Subdistrict, Karanganyar Regency, has geosphere conditions that have the potential to be developed for agribusiness crops, but are prone to landslides. In it’s development, it is necessary to integrate considerations of productivity and land sustainability by considering the carrying capacity of the land through the identification of landslide vulnerabilities. The objectives of this research are: (1) To determine the vulnerability of landslides in the Ngargoyoso District, (2) To determine the direction of land conservation for sustainable agricultural land development in Ngargoyoso District. The unit of analysis is in the form of land unit which is the result of overlapping between rock, soil, slope and land use units. The method of determining landslide vulnerability uses the scoring method of landslide determining parameters. The results of the research were (1) high landslide susceptibility area of 4,797.25 hectares (78.13%), moderate landslide susceptibility area of 1,343.26 hectares (21.87%), and (2) conservation directions in the form of zoning for seasonal agricultural land and manufacturing. terracing by paying attention to the slope and depth of the solum.<strong></strong></em></p>
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".