EXPERIMENTS WITH NOTARIES ABOUT THE SEMIOLOGY OF 3D CADASTRAL MODELS
Bibliographic record
Abstract
Abstract. Based on the hypothesis that 3D cadastral models are helpful for notaries, this study investigates the performance of visual variables for the visualization of 3D models. The approach undertaken uses face-to-face interviews with notaries involved in co-ownership establishment. A 3D geometric model of a complex condominium building is used as the studied case to which a selection of visual variables is applied. Thirty visual solutions are tested against six notarial visualization tasks and notaries are asked several questions. Based on the preliminary responses, we can now say that colour is the visual variable most appreciated by notaries, regardless of the visualization task. The use of transparency is helpful in many cases, more specifically when reading annotation (official measures). However, confusion arises when too extensive geometry of 3D lots is viewed simultaneously, and unnecessary when the geometry of the lots is fully visible. Moving the position of the geometry of a group of lots (by floor for example) looks also promising. Although this interview-based approach is subjective and empirical, it helps us to better consider the end-user's interests and take into consideration their professional opinion and requirements. The 30 visual solutions produced during these first experiments constitute a useful foundation for further analysis.
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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 | 0.011 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".