Data Acquisition and Lessons Learnt from Geophysical Remotely Piloted Aircraft System (RPAS) Surveys in Northern Canada
Bibliographic record
Abstract
This paper discusses data acquisition and lessons learnt during a geophysical Remotely Piloted Aircraft System (RPAS) survey in Northern Canada. The goal of the project was to identify areas that may have buried waste materials using a magnetometer attached to an RPAS. RPAS aeromagnetic surveys have a good coverage (and coverage rate) and high resolution compared to conventional walking terrestrial surveys (Everett 2007, Nieldzielski 2018 and Walter at al. 2019) especially in remote locations with variable terrains. The RPAS was able to cover an area of 55 hectares over two days of surveying. Twelve major and twelve minor anomalies were identified in the magnetometer data. Photogrammetry was also collected over a 315-hectare area. This included a high resolution ortho-mosaic as well as a digital terrain model and a digital surface model. The RPAS magnetometer survey was highly successful at identifying areas with strong magnetic signatures as well as areas with weaker signals. The major anomalies identified all have very strong signals with the clear high and low pattern that is expected. The photogrammetry provided high-quality imagery of the area as well as surface models and greatly assisted in the interpretation of the magnetic signatures. RPAS surveys in northern parts of Canada have specific logistic and acquisition challenges that affect the operation of the survey but do not affect the quality of the data.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".