Bibliographic record
Abstract
Fold, illumination, offset distribution and azimuth distribution were evaluated for PS survey design for two different projects. The first was planned to image an interval of interest from 380 m to 425 m depth, for the Paskapoo Formation, located in the Priddis area, near Calgary, Alberta. Orthogonal and slant geometries were tested with different parameters. Good results for the converted wave 3D design were found using a slant geometry design with receiver interval and source interval of 10 m, a receiver line interval of 50 m, a source line interval of 25 m and a maximum offset of 400 m. The slant geometry design gave better offset and azimuth distributions than the orthogonal geometry design. Illumination was similar in both types of geometries. Optimization of these parameters was reached by changing the receiver and source line intervals to 40 m, balancing quality requirements and cost related to the increase of number of shots when using 25 m as the source line interval. This design was done using CREWES QuadDes software. A second case study was undertaken where orthogonal and slant designs were tested for a project area with a deeper target, at 2160 m depth. Real situations were taken in to consideration such as moving source lines to pre-existing cut lines and the effect of obstructions presented by lakes in the area of the survey. After the analysis, the orthogonal geometry design was chosen with 360 m source line interval, 240 m receiver line interval and 60 m source and receiver station intervals. The patch selected was 26 lines with 100 receivers per line to have an aspect ratio of about unity for optimum data inversion. This design was undertaken using OMNI software.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".