A Worldwide Historical Dam Failure's Database / Base de données de ruptures historiques de barrages à travers le monde
Bibliographic record
Abstract
This database compiles a total of 3,860 cases of dam failure worldwide, regardless of the type of dams (man-made dam, retention dam, temporary dam, natural dam, etc.), either the type of structure (concrete dam, embankment dam, etc.), the type of failure (failure due to internal erosion, overtopping failure, etc.) or the properties of the dams (dam height, reservoir capacity, etc.). Overall, the dataset compiled for each case of rupture aims to standardize data collection for the different types of dams identified and to facilitate the international collection by setting guidelines. Notice that in this first version, 40 variables have been used. The record failure description obtained through this investigation work might be a door opening in various fields of research, such as hydrology, hydraulics, and dam safety. In regard to the magnitude of the task, it is possible that duplicates and/or missing data still remain. The author encourages readers to share their suggestions, remarks and/or questions to the author. If inconsistencies and/or more failures are observed by the user, a list can be sent via email in order to transmit observations / corrections / uncertainties / questions. An updated version will be deposited at undetermined frequencies. Its general structure can easily be adapted to the needs of the user and will, however, be updated over the years. Cette base de données compile un total de 3 860 cas de rupture de barrages à travers le monde, indépendamment du domaine d’application (barrage construit par l’homme, barrage de rétention, barrage temporaire, barrage naturel, etc.), du type d’ouvrage (barrage en béton, barrage en remblai, etc.), du mode de rupture (rupture par érosion interne, rupture par submersion, etc.) et des propriétés des ouvrages (hauteur du barrage, capacité du réservoir, etc.). Globalement, le jeu de données traduisant un total de 46 variables vise à uniformiser la collecte de données pour les différents types de barrages recensés et à faciliter la collecte à l’internationale à l’aide de balises. La structure proposée permet d’ouvrir la porte à de nouvelles études, et ce, dans divers domaines de recherche, tels que l’hydrologie, l’hydraulique et la sécurité des barrages. Comme il s’agit de la première version, il est possible que des doublons et/ou que de nouveaux cas de rupture soient mis en évidence. Si des incohérences ou nouvelles données sont remarquées par l’utilisateur, il est invité à transmettre ses observations/corrections/incertitudes à l’auteure (via courriel). De plus, bien que sa structure puisse aisément être adaptée en fonction des besoins de l’utilisateur (via the open XLSX file), celle-ci sera également mise à jour au fil des années. Notez que des mises à jour seront déposés à des fréquences indéterminées pour assurer le suivi des enregistrements.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.029 |
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".