Bibliographic record
Abstract
Abstract In the last several years, seismic-curvature attributes have been shown to be very useful in delineation of folds, flexures, and faults. Although many curvature measures have been introduced, the most-positive and most-negative principal curvatures (k1 and k2) are found to be the most useful. All other curvature measures can be derived from the two principal curvatures. For example, the components of apparent curvature projected parallel to the dip azimuth and strike of a dipping plane are useful in given tectonic and stress settings. Euler curvature is a generalization of the dip and strike components of curvature in any user-defined direction, as applied to the interpretation of surface-seismic data. This attribute is useful for interpretation of lineament features in desired azimuthal directions, for example, perpendicular to the minimum horizontal stress. If a given azimuth is known or hypothesized to be correlated with open fractures or if a given azimuth can be correlated with enhanced production or effective horizontal drilling, a Euler-curvature intensity volume can be generated for that azimuth, thereby high-grading potential sweet spots.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".