Bibliographic record
Abstract
In September 2011, CREWES recorded a data set in southern Alberta near Hussar. The purpose of this survey was primarily to compare receiver types (four types of receivers) and source types (four types of sources) at very low frequencies. Data processing by CGGVeritas resulted in a final section for 10Hz vertical component geophones and dynamite source and this was used in this study. Three wells intersected this line, two near the south (12-27 and 14-27) and one towards the north (14-35). An overburden and underburden, derived from the stacking velocities, was applied to the sonic logs. An overburden and underburden was also applied to the density log using the stacking velocities and Gardner’s equation. The next step was to estimate a wavelet by fitting a fourth order polynomial to the decibel amplitude spectra of a seismic average trace.. Synthetic traces were then prepared at each well location. A rough tie of well 1227 was completed and the seismic data was balanced with a time-variant scaling operator. To create a good tie between the seismic data and the synthetic traces the sonic logs of the wells were stretched until key events matched. An average trace was prepared from averaging five traces located at each well location after the traces were aligned as a slight dip is evident in the seismic data. An average synthetic trace was prepared by aligning, balancing and averaging the synthetic seismograms at each well location. The reflectivity and Impedance for these wells were also aligned and averaged. It is helpful to have a reference impedance section which was created by a weighted average of the well impedance logs. This study found that producing well ties that match the seismic data is not trivial and is very important for accurate inversions. StretchWell and WaveletEstimator were two programs created for this study with graphical user interfaces used in tying the wells. StretchWell is used to modify the sonic logs and WaveletEstimator is used to create wavelets. Each of these programs is explained at the end of this paper.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.044 | 0.012 |
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".