Bibliographic record
Abstract
Civil infrastructure is important to ensure the ongoing functionality of human living environments. However, in North America, much of the infrastructure is aging and requires continuous monitoring and maintenance to ensure the safety of people. Traditionally, visual inspection has been carried out to monitor the health of such structures. However, assessments require trained inspectors, and monitoring methods are difficult due to the size and location of the infrastructure. Recently, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has been growing in popularity, and research has been conducted concerning the use of UAVs for the visual inspection of infrastructure. However, UAV inspection requires skilled pilots and the use of a global positioning system (GPS) for autonomous flight. Unfortunately, for some locations, a GPS signal cannot be reached for autonomous flight of the UAV. For example, the GPS signal on the inside of a building or underneath a bridge deck is unreliable, but these locations also require inspections to ensure structural health. In order to address this issue, autonomous UAV methods using ultrasonic beacons have been proposed. Beacons are able to provide positional data allowing UAVs to perform the autonomous mission. As an example of structural damage, we report the successful detection of concrete cracks using a deep convolutional neural network by processing the video data collected from an autonomous UAV.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".