Bibliographic record
Abstract
Dipmeter is a well logging tool for measuring formation dip, that is, the angle and the direction of the tilt of the underground formation. It does this by measuring focused microresistivity in a few places on the circumference of the hole. If the formation is dipping the resulting measurements will be displaced in relation to each other. From these displacements one can compute the dip by applying standard formulas of analytical geometry. However, finding these displacements is far from a standard problem. The need for effective reservoir description poses ever great challenges and necessitates the development of better, more stable and precise dip computation algorithms. This paper describes a development environment which was designed and programmed specifically to develop and test dip computation algorithms. The author describes the geological considerations that went into the design and their implementation. The author also briefly discusses the results achieved using this environment, namely, new dip computation algorithms. The immediate benefit of the DATE workbench is therefore the development of new dip computation algorithms. In addition, it can serve either as a base or as a model for developing processing algorithms for other tools.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".