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
This paper deals with automatically learning the spatial distribution of a set of measurements: images, in the examples presented here. The solution to this problem can be viewed as an instance of robot mapping although it can also be used in other contexts. We examine the problem of organizing an ensemble of images of an environment in terms of the positions from which the images were obtained, and where only limited prior odometric information is available. Our approach employs a feature-based method derived from a probabilistic robot localization framework. Initially, a set of visual landmarks are selected from the images and correspondences are found across the ensemble. The images are then localized by first assembling the small subset of images for which odometric confidence is high, and sequentially inserting the remaining images, localizing each against the previous estimates, and taking advantage of any priors that are available. We present experimental results validating the approach, and demonstrating metrically and topologically accurate results over two large image ensembles, even given only four initial ground truth poses. Finally, we discuss the results, their relationship to the autonomous exploration of an unknown environment, and their utility for robot localization and navigation.
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 | 0.000 | 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