Landslide and Environmental Risk from Oil Spill due to the Rupture of SOTE and OCP Pipelines, San Rafael Falls, Amazon Basin, Ecuador
Bibliographic record
Abstract
A landslide generated an environmental risk due to a provoked oil spill on April 7, 2020, with the SOTE and OCP pipelines rupture. This research aims to determine the areas susceptible to landslides in the river basin Quijos of the Coca River and estimate the environmental risk from exposure to the oil spill. A water analysis of the Coca River was performed by using the Mora-Vahrson method and GIS tools. The subsequent water sampling was probabilistic in a simple random way, and the analyzed parameters were oils and grease, Ba, Cd, Cr, BOD, COD, TPH, OD, Pb, and SST. The results show that 61.17% (572.68 km2) of the total studied area (936.19 km2) is susceptible to landslide hazards. In detail, 0.25% (2.34 km2) of the area is considered to be of very high susceptibility, 26.72% (250.12 km2) of high susceptibility, 11.82% (110.66 km2) of moderate susceptibility, and 0.04 (0.37 km2) of low susceptibility. Four of them were within the permissible limits from the ten analyzed parameters, which correspond to Ba with 0.70 mg/L, OD with 7.4% of saturation, BOD5 with 2 mg/L, and COD with 25 mg/L. The other six parameters, including oils and fats, exhibited a significant increase in concentrations after the oil spill, yielding Cd 0.05 mg/L, total Cr 0.45 mg/L, TPH 0.20 mg/L, Pb 0.20 mg/L, and SST 20%. These results are outside the permissible limits, meaning that the river waters are contaminated.
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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 | 0.001 | 0.001 |
| 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".