A Web-Based Visual and Analytical Geographical Information System for Oil and Gas Data
Bibliographic record
Abstract
With the development of strategic oil and gas assets, massive spatiotemporal oil and gas data have been accumulated. Application systems that assist in the storage and management of the voluminous and complex oil and gas datasets are in high demand. The voluminous and various data should be leveraged and turned into information for business decision-making and operation assistance. In this paper, we propose a set of visual analytic methods that specialize in oil and gas data; and, we develop a web-based oil and gas data management, visualization and analytical system, called Oil and Gas Visual Exploration System (OGVES). With OGVES, complex and multi-sourced oil and gas data can be stored, searched, filtered, and represented. As a web-based system, the OGVES provides more accessibility, convenience and efficiency than traditional desktop systems. Spatial scales and temporal primitives contained in oil and gas data are discussed. Different visualization methods are then presented to explore and represent spatiotemporal features of the oil and gas data. Various case studies demonstrate the usability of the system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".