Microdrones in field-based structural geology: a photogrammetry and fracture quantification case study from the North Mountain Basalt, Nova Scotia, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drone use in geoscience research and teaching is becoming widespread, with diverse applications documented. Many studies favour consumer-level drones, however, recent developments in so-called “microdrones” (takeoff weight < 250 g) necessitate further investigation to determine possible benefits, limitations, and future developments. Microdrone deployment is often advantageous in numerous jurisdictions due to fewer regulations, lower cost, and simple transportation. In this study, we deployed a DJI Mini 2 microdrone to study the ca. 201 Ma North Mountain Basalt (NMB) exposed in coastal outcrops along the Bay of Fundy, Nova Scotia, Canada. We report benefits of the microdrone as a field aid with three related approaches: (1) general site location and characterisation, (2) drone-based photogrammetry using ArcGIS Drone2Map, and (3) quantitative fracture mapping using FracPaQ. Application of these methods showed that microdrone-acquired imagery from the NMB exposures provides a valuable resource for interpretation post-fieldwork. The microdrone-derived data show two near-perpendicular fracture sets in the NMB: ∼NNE–SSW and ∼ESE–WNW, with some variation along the coastline. Overall, we determined that microdrones offer field-based geoscientists a valuable tool due to quick deployment, a simple image capture process and relatively straightforward data processing, and thus predict that this approach to enhancing fieldwork will continue to advance.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it