PACIFIC ambient noise dense seismic survey at Marathon deposits
Bibliographic record
Abstract
Between September 17th and October 26th of 2018, at the Marathon test site(Ontario, Canada), a 1025 sensor passive seismic survey was completed. The aim of the acquisition was to extract body waves from ambient noise to image the subsurface. Indeed, Once body waves have been extracted, the data can be processed to acquire reflection seismic sections, following industry standard methods as commonly applied in the hydrocarbons sector. The sensors equipment was rented from SAExploration. 1025 sensors were successfully deployed; however, only 1013 were recovered. The loss of sensors was due to animal activity or being buried by a rock slide. The grid design was composed of two overlapping grids, a 416-sensor array and a 609-sensor profile line. The array had a grid spacing of 150 m, while the profile line had a grid spacing of 50 m. Both grids designs were configured along the main noise source of Lake Superior in the direction of 250deg to the west. The sensors selected for the survey were ZLAND Fairfield vertical direction sensors with a 9 Hz range. Once the sensors were retrieved, they were shipped back to SAExploration for download. The data was successfully downloaded and shipped to Sisprobe for conversion to MSEED format.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".