Random Field Modeling of Rainfall-Induced Soil Movement
Bibliographic record
Abstract
Natural oil and gas transmission pipeline networks often traverse regions where potential slow ground movements may affect pipeline structural integrity. One of the primary causes of slow ground movement in any region involves the duration, amount, and intensity of rainfall. The phenomenon of rainfall-induced slow ground movement is characterized by both spatial and temporal variability, and involves uncertainties that are best modeled using a probabilistic methodology. A random field modeling strategy is formulated in this study, in which spatial and temporal correlations between rainfall and ground movement are accounted for. The random field formulation advanced in the current study has a number of significant features and capabilities, including modeling the spatial and temporal relationship between rainfall and slope movement for specified pipeline routes, predicting the likelihood of exceeding slope movement thresholds for various precipitation levels and intensities, and providing maps of risk for slope movement, which can be used as a guide in pipeline route planning, selection, and adaptation strategies for the design and maintenance of oil and gas infrastructure. These capabilities have been implemented and encapsulated into the software tool VSLOPE, which has been tested using monthly rainfall and field data for various locations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".