Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary In 4D land seismic and especially for Permanent Reservoir Monitoring (PRM), changes of the near-surface induce unwanted signal variations that interfere with the 4D signal recorded from the reservoir. A three-month PRM pilot was carried out for Shell on the Peace River heavy oil field in Alberta, Canada in 2009. During this period, reservoir production was monitored using active buried sources and buried receivers. We took advantage of this continuous seismic recording to extract surface waves from recorded ambient noise using cross-correlation techniques. Surface wave tomography is then applied to produce daily time-lapse surface wave velocity maps that monitor velocity variations within the near-surface. We provide an image of the shallow subsurface velocities showing generally higher values in the southern part of the area. This pattern correlates fairly well with the known presence of swamp (muskeg) in the area and the wells pad location. Calendar observation of velocity maps shows stronger variation at low frequencies with good spatial coherence. In the case of PRM and continuous seismic monitoring, these findings could help to discriminate, at least qualitatively, contributions due to near-surface variations from actual reservoir 4D variations.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it