Bibliographic record
Abstract
The use of Unmanned Aerial Vehicles (UAV) or Unmanned Aircraft Systems (UAS) has been attracting much attention among geomatics and geospatial professionals recently. The typical UAS consists of an autonomous aircraft with a highly advanced navigation system and a high quality digital camera. The systems are often used in situations where a more traditional form of data collection is either impractical or impossible due factors such as project size, high risk environments, accessibility issues etc. The system will rapidly capture digital images of an area of interest which are subsequently post processed along with flight log files to produce a surprisingly high quality orthomosaic and digital elevation model (DEM). Image resolution and model density are controlled by flight altitude and image overlap, however georeferencing of data can be greatly enhanced through the use of survey quality ground control targets placed pre-flight. These targets will be identified by the user in the image processing software post-flight and will serve to calibrate the orthomosaic and the resulting DEM. Manitoba Infrastructure and Transportation (MIT) have acquired a UAS and intend to employ this exciting new technology for a number of data collection applications. The following paper represents some background on our investigation into this technology from product research to product acquisition in our ongoing efforts toward the development of a BETTER, FASTER, SAFER and more cost effective method of data collection. For the covering abstract of this conference see ITRD record number 201310RT334E.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.039 |
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".