Multi-modal Sensor Data Resource of Canadian Critical Electrical Infrastructure
Bibliographic record
Abstract
We collect a unique data resource from multiple sensor modalities for the purpose of training and evaluating algorithms for monitoring electrical critical infrastructure within the Canadian environment. Two different sensor modes were considered for inclusion in the data resource: colour images and 3D Light Detection And Ranging (LiDAR) sensors. This research examined these different sensors for their potential use in monitoring electrical critical infrastructure, such as poles, high-tension wires, and transformers. A multimodal data acquisition system was assembled using commercially available sensors. The acquisition system was deployed on a ground vehicle in the National Capital Region to collect multimodal data of power critical infrastructure along Canadian road corridors. Data was collected and registered in both time and space in order to enable multi-sensor fusion. Data Description: A total of 1.77 GB of data captured from total of 440 scenes, for each scenes we included images, Segmentation label and LiDAR data.The image data was annotated for semantic segmentation with five different classes. We have 440 scenes and for each of scene we have following data: |___Image |___Labels | |__NPY | |__PNG |____LiDAR
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".