Bibliographic record
Abstract
Three studies were conducted to evaluate the behavior of Vp/Vs ratio error in beds with different thickness. In the first study, a synthetic model was created to understand how the interval time would affect the calculation of the interval Vp/Vs and an equation was derived to estimate the percent error in the uncertainty of the ratio calculation. In a second study, the seismic data at Hussar, Alberta showed that Vp/Vs error increased as the analysis time window interval decreased. In addition, the percent error of the Vp/Vs values due to uncertainty was examined and it is suggested to use isochron intervals greater than 150 ms in PP time for robust results. Interval Vp/Vs analysis for data with intervals greater than this isochron have low uncertainty. In the third study, at Spring Coulee, Alberta, results showed that horizon pick adjustments can reduce error uncertainty by a small factor. In this and the previous studies, it was confirmed that beds with small thickness can have a significant impact on interval Vp/Vs uncertainties, which could lead to erroneous results. Additionally, all studies indicated that interpreters should work with bed intervals greater than 150 ms in PP times and perform horizon adjustments for more precise results.
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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".