Visual modeling of business problems: workflow and patterns
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
Computer-based business analysis relies on models, or algorithmic representations of the business process. Real-life business problems can become very complex, which creates difficulties in generation, analysis, testing, and the actual use of the models. The paper discusses a proposed solution: the visual modeling workflow. A diagram or a group of diagrams represent each step within this workflow. The visual modeling process can be simplified by applying patterns or problem-solution formulas. Such modeling patterns include decoupling, encapsulation, visualization of user workflow, multi-layer visual representation of the calculation logic, and early identification and visualization of uncertainties. The patterns are applied to the visual modeling workflow, which include high level conceptual modeling, using Domain Models and Calculation Diagrams to visualize the calculation logic, visualization of testing and consolidations, and visualization of results of probabilistic analysis and simulation. The described methodology is used in a number of Schlumberger's software application.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it