EXPERIMENTS WITH NOTARIES ABOUT THE SEMIOLOGY OF 3D CADASTRAL MODELS
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
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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