Significant Canadian earthquakes 1600-2017
Bibliographic record
Abstract
This Open File provides the most up-to-date information on the significant earthquakes of Canada for the period 1600 to 2017 inclusively. In addition to slight adjustments to a previously published Open File (Lamontagne et al., 2007), the current Report adds some significant earthquakes that occurred between 2007 and 2017 inclusively. In light of ongoing research, a number of updates were also included, together with the justifications for the changes. When available, maps based on macroseismic surveys or Did-You-Feel-It reports were also joined. As in the previous Open File Report, earthquakes are considered significant if their magnitude, estimated from felt reports or scaled from records, exceeded 6.0 on the Richter scale or if they had been felt by many Canadians at Modified Intensity VI or stronger. A total of 172 events were selected for the period 1600-2017. The information is provided in a Microsoft Excel sheet that provides for each event: the source information (Origin Time, Latitude, Longitude, Depth, Region, Magnitude); the impact (associated landslide(s), tsunami, damage to buildings, deaths, Maximum Modified Mercalli Intensity in Canada); a description of the event; the source of information and web links for additional information in English and French.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.014 |
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".