Automated Spatial-Semantic Modeling with Applications to Place Labeling and Informed Search
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a spatial-semantic modeling system featuring automated learning of object-place relations from an online annotated database, and the application of these relations to a variety of real-world tasks. The system is able to label novel scenes with place information, as we demonstrate on test scenes drawn from the same source as our training set. We have designed our system for future enhancement of a robot platform that performs state-of-the-art object recognition and creates object maps of realistic environments. In this context, we demonstrate the use of spatial-semantic information to perform clustering and place labeling of object maps obtained from real homes.This place information is fed back into the robot system to inform an object search planner about likely locations of a query object. As a whole, this system represents a new level in spatial reasoning and semantic understanding for a physical platform.
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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 | 0.000 | 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 | 0.000 |
| 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