Landslides in Rio de Janeiro: The role played by variations in soil hydraulic conductivity
Bibliographic record
Abstract
Abstract Landslides are natural processes triggered by complex relationships between many conditioning factors. In Rio de Janeiro city, intense rainstorms combined with steep slopes, usually occupied by slums, create favourable conditions for landslide generation. In general, slope failures are related to high positive pore‐pressures inside soil mantles, which are associated with topographic hollows and hydraulic discontinuities inside the deep weathered profile. This study aims to characterize the role played by spatial variations in saturated soil hydraulic conductivity Ksat on landsliding in the Papagaio Basin (2·5 km2), triggered by intense rainstorms (250 mm in less than 2 days) in February 1996. Field measurements, using the Guelph permeameter, were conducted along soil profiles (up to 5·0 m depth), around and inside landslide scars representing different geomorphological domains. Also, soil physical properties (grain size, total porosity, macroporosity, microporosity, etc.) were evaluated at the 87 sites where Ksat was measured. Despite the many geological and pedological variations inside the basin, including changes in lithology, fracture density, vegetation type and succession stage, soil characteristics and weathering stage, and human activities, 94% of the Ksat values varied by only two orders of magnitude (1·0 × 10−6–9·0 × 10−5 m s−1). However, abrupt changes in Ksat were observed along some of the soil profiles studied, e.g. an increase of two orders of magnitude in only 0·30 m. These hydraulic discontinuities may contribute locally to the development of high pore pressures during intense rainstorms, leading to landsliding. The results also suggest that different Ksat variation patterns coexist inside the basin, which should be considered in mathematical models of landslide prediction. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".