Analyses of earthquakes in the Western Quebec Seismic Zone using data from the United States Transportable Array
Bibliographic record
Abstract
The present research investigates the efficacy of the additional seismic stations that were deployed in the Western Quebec Seismic Zone (WQSZ) as part of the US Transportable Array (USTA) during 2013-2015 in the determination of earthquake source parameters in this zone. The study is concentrated on the determination of hypocentre locations of three sets of data: the aftershock sequence of the 2010 Val-des-Bois earthquake; the aftershock sequence of the 2013 Ladysmith earthquake; and, 18 events of magnitude 3.0 or greater that occurred in the WQSZ from January 2013 to mid-July 2015. An attempt is also made to estimate the focal mechanisms of all events with MN3 that occurred in the WQSZ during 2013-2015. The relocated hypocentres of the two aftershock sequences using the double-difference algorithm through the hypoDD program and the joint hypocentre determination through VELEST software shows insignificant changes in focal depths (toward shallower depths) and only slight variations in the trend of the seismicity (toward the southwest for the Val-des-Bois aftershock and toward the south-east for the Ladysmith aftershock). Relocation of 18 magnitude 3+ events using the standard travel times method demonstrates more reliable focal depths when both the Canadian National Seismograph Network and the USTA data are used. Although favourable coverage provided by the additional USTA can be slightly more effective in obtaining more accurate hypocentres for small earthquakes, the focal mechanism solutions are not remarkably changed when more data from the combined seismic network are used. Dariush Motazedian, for their continuous support, patience, motivation and immense knowledge. They consistently allowed this study to be my own work, but steered me in the right direction whenever they thought I needed it. Also, I must express
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".