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
Collecting dense range measurements in uncontrolled environments is a challenging problem as lighting and surfaces' texture significantly influence the quality of the measurements. Instead of concentrating on improving a specific type of range sensors, the overall quality of the sensing can also be enhanced through the development of a mechanism that combines various range sensing technologies to form a multi-modal range sensor. Although many different multi-modal systems have been investigated, the problem of merging datasets have hinder engineers from producing unified data. Two major approaches have been used to rectify this problem: system calibration of the multi-modal system and data fitting of all datasets into a single model, which the latter is more widely used. The lack of multi-modal system calibration approaches is due to their complicated and lengthy nature, where individual calibration approaches must be applied to each subsystem and then applied between subsystems of the multi-modal range sensor. To alleviate the problems in multi-modal system calibration, straightforward and generic guidelines for calibration are defined and applied to an in-house multi-modal system built from a laser range finder system, two active triangulation systems using structured lighting, and a stereovision system. This paper addresses the system's intra- and inter-calibration processes and presents renderings of datasets collected with the calibrated multi-modal range sensor without the use of data fitting. From these results, the potential benefits of multi-modal calibration that reduces the need of data fitting and the advantages of merging subsystem's strengths to complement other subsystem's weaknesses are put in evidence
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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