Hubungan Karakteristik Demografi Terhadap Pengetahuan SIBAT Di Bantaran Sungai Bengawan Solo
Bibliographic record
Abstract
ABSTRACTBackground: Climate change in Indonesia is strongly influenced by 3 basic climate patterns: monsoon, equatorial and local climate systems which cause dramatic differences in rainfall patterns, tending to give rise to a high potential for various types of hydrometeorological disasters, such as floods, flash floods, droughts, weather extreme, extreme waves. The community-based early warning system for flood disaster preparedness (SIBAT) is one of the means for preventing flood disasters.The aim of this research is to describeThe relationship between the characteristics of sibat cadres and knowledge about community-based early warning system strategies for flood disaster preparedness (sibat) on the banks of the Bengawan Solo River, Blora Regency. Research methods This is a quantitative approachcross sectional with a total sample of 110 SIBAT cadres selected using the Slovin method. Data was collected using a questionnaire sheet, then analyzed usingChi Square. The research results show that from3 The independent variable is significantly related to the dependent variable, the age variable with pvalue 0.013, gender variable with pvalue 0.071, education level variable with pvalue 0.013. which means that each variable has a strong relationship with the dependent variable, namely with knowledge about the Community-based Disaster Information System on the banks of the Bengawan Solo River.Keywords: SIBAT cadres, disaster preparedness, floods
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".