Generation of a Susceptibility Map with Geomechanical Soil Data in the Southwestern Zone of Loja Ecuador
Bibliographic record
Abstract
This study's objective is to develop a susceptibility map based on the geomechanical characterization of soils in the southwestern region of Loja, with a special focus on assessing their bearing capacity.This project is critical for the planning and designing of infrastructure, particularly in areas with complex geological conditions.Geomechanical characterization involves identifying and analyzing various physical and mechanical properties of the soil, such as texture, structure, density, and strength, through a combination of field studies and laboratory tests.The methodology begins with the collection of geological and geotechnical information, followed by specific geotechnical studies.For this investigation, Dynamic Cone Penetrometer (DCP) and Standard Penetration Test (SPT) were employed, alongside lithological and geological surveys to identify different formations and lithologies in the area.The results will allow the creation of a geomechanical characterization map, which will serve as an essential tool for engineers, architects, and urban planners.This map optimizes structural design and minimizes risks associated with geotechnical failures.In summary, the bearing capacity map, complemented by laboratory and field studies, demonstrates that the area's lithology is suitable and offers favorable conditions for expanding the urban cadastral boundaries in Loja.
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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 | 0.004 | 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) | 0.001 | 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".