PENYUSUNAN RENCANA PEMULIHAN SEBELUM TERJADI BENCANA (PRE DISASTER RECOVERY PLANNING) SEBAGAI UPAYA MEMITIGASI RISIKO KEMUNGKINAN KEJADIAN BENCANA TANAH LONGSOR DI KABUPATEN PURWOREJO
Bibliographic record
Abstract
Landslides is a high-risk threat in the Kaligesing district area, Bener district and Bruno district in Purworejo Regency. This refers to RT/RW Regency documents, History of landslides and results of field searches conducted by the service team. This activity aims to compile a plan for recovery before the disaster (Pre Disaster Recovery Planning) will occur a possible landslide in Purworejo Regency. Method of using activities ECLAC (Economic Commission for Latin America and the Caribbean) which is used analyzes analyzes each sector of damage and losses and the method of assessment of damage and losses (Damage and Loss Assessment). This activity was carried out in 5 stages which were completed in a duration of 6 months. The results of the high impact area of landslides occurred in 31 villages with 108 hamlets in the Kaligesing District, Bener District and Bruno District, as well as the potential threat of disaster for a total of 60,515 inhabitants living in the area. Evaluation of damage and loss of impact from the possibility of a landslide in Purworejo Regency worth Rp 417.617.458.000, with the total post-disaster recovery needs worth Rp 207.146.141.000. The Recovery Plan Document Before a Disaster can be legalized by the Purworejo Regency Government as a disaster planning document, action plan for rehabilitation and construction of landslides, and guidelines with legal force in the implementation of rehabilitation and construction of landslide disasters in Purworejo Regency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.036 | 0.003 |
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".