Innovative Strategies for Sustainable Agroforestry in Landslide-Affected Sukajaya, Bogor, Indonesia
Bibliographic record
Abstract
Road construction involves filling and excavation procedures, leaving slopes susceptible to geological, hydrological and geomorphological factors, increasing the risk of instability and threatening road infrastructure and surrounding communities.The Chimborazo Province in Ecuador, due to its geographic location, diverse topography with structurally complex soil strata and variations in subsurface stratigraphy, requires detailed investigations focused on geophysics and in situ soil testing.This study assesses slope stability by correlating geophysical and soil tests to generate slope stability measurements.The applied methodology is based on the analysis of surface conditions, application of geophysical exploration, geotechnical characterisation, analysis and slope stability strategies.In the characterisation of the slope, three altered test pits were made where it was determined that there are metavolcanic materials, gravel and colluvial geologically.By correlating geophysical tests, it was determined that in the centre of the slope, the most critical apparent resistivity values (90-140Ω•m) correspond to colluvial deposits.For stability, it is proposed to terrace the slope surface with crown ditches at each level due to the presence of water holes and, in the most critical areas, to place reinforcement mesh over the vegetation.The existence of seepage zones on the slope was determined as a triggering factor for instability.These strategies serve as a basis for decision-makers in improving road connections and socioeconomic development in Andean rural communities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".