POST-SEISMIC RECONSTRUCTION IN LE TEIL FOLLOWING THE NOVEMBRE 11TH 2019 SEISMIC EVENT
Bibliographic record
Abstract
The earthquake of November 11, 2019 has strongly affected the town of Le Teil (8.700 inhabitants) in Ardeche, France. A quarter of the 2.800 buildings were damaged to the point of making it necessary to issue evacuation orders for the safety of their occupants. As part of an action led by the French Ministry of Ecology and Solidarity Transition, BRGM was asked to collect information to quantify the progress of the reconstruction process and to identify the institutional and financial levers that contribute to it. Through this project and on the basis of building diagnosis carried out during the emergency phase, the building reconstruction of the commune of Le Teil was studied at different scales: communal and infra-communal. From the reference state of the damaged building database and in order to evaluate the progress in terms of structural reconstruction, the buildings were assigned a reconstruction stage on the basis of annual visual examinations lead from the outside. In addition, a set of physical-urban-building indicators has been identified in order to provide quantitative information that evolves over time such as the number of evacuation orders (issued and lifted), the number of requests for urban planning permission or the number of buildings that have started a reconstruction process. In the meantime, a more in-depth analysis of the urban planning aspect and the perception of the population in relation to this reconstruction has been carried out through a multi-thematic analysis. These actions raised important questions over building reconstruction particularly through a « Build Back Better » approach aspect following the building construction standards. The focus point is turned towards vulnerabilities and territorial capacities, two main components of the reconstruction process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".