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 new method is presented for the efficient and reliable pose determination of 3D objects in dense range image data. The method is based upon a minimalistic Geometric Probing strategy that hypothesizes the intersection of the object with some selected image point, and searches for additional surface data at locations relative to that point. The strategy is implemented in the discrete domain as a binary decision tree classifier. The tree leaf nodes represent individual voxel templates of the model, with one template per distinct model pose. The internal nodes represent the union of the templates of their descendant leaf nodes. The union of all leaf node templates is the complete template set of the model over its discrete pose space. Each internal node also encodes a single voxel which is the most common element of its child node templates. Traversing the free is equivalent to efficiently matching the large set of templates at a selected image seed location. The method was implemented and extensive experiments were conducted for a variety of combinations of tree designs and traversals under isolated, cluttered, and occluded scene conditions. The results demonstrated a tradeoff between efficiency and reliability. It was concluded that there exist combinations of tree design and traversal which are both highly efficient and reliable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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