Bibliographic record
Abstract
The datasets are original and specifically collected for research aimed at reducing registration errors between Camera-LiDAR datasets. Traditional methods often struggle with aligning 2D-3D data from sources that have different coordinate systems and resolutions. Our collection comprises six datasets from two distinct setups, designed to enhance versatility in our approach and improve matching accuracy across both high-feature and low-feature environments. Survey-Grade Terrestrial Dataset: Collection Details: Data was gathered across various scenes on the University of New Brunswick campus, including low-feature walls, high-feature laboratory rooms, and outdoor tree environments. Equipment: LiDAR data was captured using a Trimble TX5 3D Laser Scanner, while optical images were taken with a Canon EOS 5D Mark III DSLR camera. Mobile Mapping System Dataset: Collection Details: This dataset was collected using our custom-built Simultaneous Localization and Multi-Sensor Mapping Robot (SLAMM-BOT) in several indoor mobile scenes to validate our methods. Equipment: Data was acquired using a Velodyne VLP-16 LiDAR scanner and an Arducam IMX477 Mini camera, controlled via a Raspberry Pi board.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.069 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".