Bibliographic record
Abstract
Landslide is one of the high frequency disasters that occur in Indonesia. The incident recurs every year with a different location. The fact that landslide hazards are used intensively for agricultural cultivation due to economic considerations. One of the efforts to mitigate this disaster is the Bioenginering implementation approach. Bioenginering activity is the application of landslide hazard area management by managing plants / vegetation. The purpose of this research is to implement a vegetative technology implementation model as an effort to mitigate landslides. Bioenginering implementation is designed with a combination of ecological and socio-economic approaches. The results of this combination are consulted with the affected community and consider various vegetation alternatives. The selected vegetation not only has an ecological function but also an economic function. With these considerations, a vegetation design is obtained with a combination of upper strata (trees), middle and lower strata. For the upper strata it is recommended to plant Petai (Parkia speciosa) and Durian (Durio zibenthinus), for the middle strata, namely Coffee (Coffea arabica) and lower strata plants are pineapple (Ananas commocus). The combination of plants such as the implementation at the field level will be accepted by the farming community, because every certain period of time the farmers will be able to harvest their crops without having to remove the plants or cut down the plants. Maintaining the level of land cover and land use has implications for maintaining the stability of soil moisture conditions which in turn can reduce the threat of landslides in landslide hazard areas.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".