Aproximity Compatibility Function Among 3-D Surfaces For Environment Modelling
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Bibliographic record
Abstract
1 Introduction 2 Determining adjacent surfaces Ramiro.Liscano@nrc.ca elgazzar@iit.nrc.ca akcwong@watnow.uwaterloo.ca Institute for Information Technology Department of Systems Design National Research Council UniversityofWaterloo Ottawa, Ont. K1A 0R6 Waterloo, Ont. N2K 3G1 CANADA CANADA This article defines a method for computing a proximity compatibility function among fragmented 3D surfaces for environment modelling. Fragmented surfaces are a common occurrence after the segmentation process has been applied to 3-D sensory data, in particular for data taken from large indoor environments. This proximity compatibility function among surfaces gives an indication on how close the surfaces are to each other based on a common gap defined between the boundaries of the surfaces. This particular approach performs most of the computations in the 2-D image plane and when required will use the 3-D information in the data. This is simpler than tackling the whole problem in Euclidean space ...
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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 | 0.000 | 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