TASK DECOMPOSITION AND LEVEL OF COMPLEXITY TO SELECT THE CONTENT OF UNDERGROUND UTILITY NETWORK MODEL
Bibliographic record
Abstract
Abstract. Accurate and efficient 3D spatio-semantic Underground Utility Network (UUN) models looks indispensable for the whole cycle of its planning, construction, maintenance, and all kinds of the decision-making process. We do believe that UUN model should be able to provide multiple representations, considering data accessibility and model comprehensibility, but how to define these levels of detail (LoD)? In this research, we made the hypothesis that LoD selection is related to the complexity of task to be performed. This paper aims at designing a decomposition method of the decision-making task and defining the level of complexity to evaluate the task. Then based on the complexity level, select the content of UUN model that is most suitable for the task with the best representation. This paper discusses the possible connections between the LoD of 3D UUN model and with decision-making tasks, providing solutions to guide decisions of model selection.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".