Discovering sensor space: Constructing spatial embeddings that explain sensor correlations
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
A fundamental task for a developing agent is to build models that explain its uninterpreted sensory-motor experience. This paper describes an algorithm that constructs a sensor space from sensor correlations, namely the algorithm generates a spatial embedding of sensors where strongly correlated sensors will be neighbors in the embedding. The algorithm first infers a sensor correlation distance and then applies the fast maximum variance unfolding algorithm to generate a distance preserving embedding. Although previous work has shown how sensor embeddings can be constructed, this paper provides a framework for understanding sensor embedding, introduces a sensor correlation distance, and demonstrates embeddings for thousands of sensors on intrinsically curved manifolds.
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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 | 0.001 |
| 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