<i>"Point it, split it, peel it, view it"</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reservoir engineers rely on virtual representations of oil reservoirs to make crucial decisions relating, for example, to the modeling and prediction of fluid behavior, or to the optimal locations for drilling wells. Therefore, they are in constant pursue of better virtual representations of the reservoir models, improved user awareness of their embedded data, and more intuitive ways to explore them, all ultimately leading to more informed decision making. Tabletops have great potential in providing powerful interactive representation to reservoir engineers, as well as enhancing the flexibility, immediacy and overall capabilities of their analysis, and consequently bringing more confidence into the decision making process. In this paper, we present a collection of 3D reservoir visualization techniques on tabletop interfaces applied to the domain of reservoir engineering, and argue that these provide greater insight into reservoir models. We support our claims with findings from a qualitative user study conducted with 12 reservoir engineers, which brought us insight into our techniques, as well as a discussion on the potential of tabletop-based visualization solutions for the domain of reservoir engineering.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 it