Bibliographic record
Abstract
This paper investigates the mapping of the impact of natural hazards as included in several databases reviewed or created by the author. These are: - The database of the contribution of the session series “Natural hazards’ impact on urban areas and infrastructure”, convened and co-convened by the first author over 15 years at the European Geosciences General Assembly. - A database created from reviews of students supervised by the authors in frame of the course “Protection of settlements against risks” at the home university. - A collection of historical photographs from the 19th century on different natural and man-made hazards from the Canadian Centre for Architecture, the archive review of which has been performed by the first author and which will be subject of a book to be published about the time of the conference. -Two reviewed collections, one from the exhibition and book on “Images of disasters” (German research) and one on the book “Illustrated history of natural disasters” which include major disasters from the beginning of the mankind. In frame of the paper maps of the spread of data will be presented, created using both arcGIS online and GoogleMaps (see https://www.google.com/maps/d/edit?mid=zpbbz3WgVMBs.k-3vhGj- -l1M&usp=sharing), comparing the source and the type of hazard, to see eventual overlappings between the databases.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".