Bibliographic record
Abstract
The datasets include interpolated input values and script can be used to reproduce the study described in the Manuscript published in Geophysical Research Letters entitled:"Machine learning-based analysis of geological susceptibility to induced seismicity in the Montney Formation, Canada."<br>Included files:<br>1. Interpolated grid files: <br>- pressure gradients [pressure_gradients.csv]<br>- Montney thickness [montney_thickess.csv]<br>- Montney Formation tops [montney_tops.csv]<br>- Debolt Formation tops [debolt_tops.csv]<br>- Phanerozoic thickness [phanerozoic_thickness.csv]<br>- local SHmax variance [local_shmax_variance.csv]<br><br>2. Raw data files:<br>- SHmax azimuths from the Western Canada (wsm_montney_abc_quality.csv)- phanerozoic shapefiles (isolines every 1000m) [digitized_phanerozoic_isolines_1000m.zip]- Cordilleran thrust and fold belt shapefile [shape_files_dist_belt.zip]- faults shapefiles (digitized from Furlong et al., 2020) [faults.zip]<br><br>3. Raw AER, NRCan and CASC earthquake catalogues. [EQ_catalogues_to_compile.zip]<br><br>4. Compiled input dataset (WELLS_INPUT.csv)<br>5. Python script with logistic regression algorithm (logistic_regression.py)<br>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.914 | 0.274 |
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; both teacher heads agree on what is shown here.
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".