Research on integrated 3D geological modeling of vector grid ——taking the core area of Tianfu New Area as an example
Bibliographic record
Abstract
The development and utilisation of urban underground space is an important part of urban construction, and urban 3D geological modelling is an important standard to evaluate the difficulty of underground space development and utilisation. At present, the urban 3D geological modelling is often the construction of a single expression of the 3D model. Existing urban integration is a fusion and integration of 3D models of different spatial locations, but there is little research on the integration of different expression models in the same spatial location, especially in the integration of 3D geological structure model and 3D geological attribute model. To solve this problem, this paper takes the core business district of Tianfu New District of Chengdu city as the research area and the urban geological survey of Chengdu city as the data base, adopts the borehole-based rapid modelling technology to build the urban 3D geological structure model, uses the grid segmentation and attribute interpolation to realise the model integration, and builds the vector grid integrated 3D geological model of ‘one model, multiple expressions’. It is helpful to comprehensively consider the problems of geological structure and geological properties.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".