Flood risk in an Andean Peruvian city: A risk evaluation and mitigation project proposal
Bibliographic record
Abstract
This study was conducted along the Disparate River, located in the Huancavelica province of Peru. Within the river's marginal area, numerous residential constructions and infrastructures are at risk of incurring material, economic, and human damage. Consequently, a disaster risk assessment was undertaken with the purpose of preventing potential harm. The primary aim of the research was to evaluate the level of flood risk posed by the Disparate River in the Huancavelica district during the year 2024. To this end, a descriptive design was employed, utilizing the risk assessment tables from the "Manual for the Evaluation of Risks Originating from Natural Phenomena," Version 02, published by the National Centre for Disaster Risk Estimation, Prevention, and Reduction. Additionally, social, economic, and environmental data were collected through various methodologies, including observation, meteorological data acquisition, mapping, random household selection, and interviews. The study analyzed hazard, vulnerability, and associated risks of potential flooding, intending to propose measures for reducing risk through both structural and non-structural interventions. Following the analysis of the data collected from fieldwork and desktop research, it was found that both the hazard and vulnerability levels were high, leading to the conclusion that the risk associated with the Disparate River is significantly elevated for the structures located along its banks. Finally, a technical proposal was presented that aims to mitigate the flood risk and its consequences in the city.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".