Radiometrics with a RPAS: Calibration Method, Surveying, and Comparison to Historical Data
Bibliographic record
Abstract
Summary Remotely piloted aircraft systems (RPAS) offer a wide range of applications for geophysical exploration. Methods developed for large-volume airborne gamma-ray spectrometry can be applied to RPAS mounted spectrometers, including calibration and post-processing. In the 2021 and 2022 field seasons, British Columbia Geological Survey used an DJI Matrice 600 Pro RPAS to collect gammaray spectrometer at 13 sites in British Columbia, Canada, over subglacial tills near known alkalic and calc-alkaline porphyry deposits. There is good agreement between BCGS high-resolution RPAS K data and existing regional-scale fixed wing data. RPAS data also agree well with existing till geochemistry and ground radiometrics The low sensitivity of the crystal, due to its low volume, requires the instrument to be flown at low altitudes above ground and to spend more time per surveyed area to achieve the equivalent statistics of a large volume detector. NASVD post-processing technique alleviates some of the limitations of the low-volume detector by increasing the signal-to-noise ratio; equivalent to increasing the volume of the detector.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".