Seismogenic Landslides, Debris Flows, and Outburst Floods in the Western United States and Canada from 1977 to 2017
Bibliographic record
Abstract
This data release is a compilation of known landslides, debris flows, lahars, and outburst floods that generated seismic signals observable on existing seismic networks. The data release includes basic information about each event such as location, volume, area, and runout distances as well as information about seismic detections and the location of seismic data, photos, maps, GIS files, and links to papers, websites, and media reports about the event. Not all record types exist for each event, and the quality of the information varies from event to event. While the SQLite3 database (lsseis.db) is the native format of this database and preserves its relational structure, for the convenience of users, we extracted summary csv files of the table of events (Events.csv), which summarizes the locations and basic information about each event included in the collection and all of the references used (references.csv) from the database. We also extracted csv files specific to each event contained in the event_data.zip file. When unzipped, it contains a folder for each event that contains .csv files that summarize the sources of information we used for the event (*_information.csv), what references were used (*_references.csv), a list of seismic detections (*_seismic_detections.csv), a list of photos and figures (*_photos_figures.csv) and a list of maps and GIS files (*_maps_gis.csv), where the * indicates the unique event id (Eid), name, and date of each event. This folder will also contain any accompanying files such as photos, maps, and seismic data (if not already archived). Not all files exist for all events. These csv files are described in the metadata. The methods used to compile the database are described in the following sections.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".