The use of Topographic-bathymetric Lidar to Enhance Geological Structural Mapping in Maritime Canada
Bibliographic record
Abstract
An airborne topo-bathymetric lidar survey was conducted in Cape John, on the north shore of Nova Scotia, Canada in September 2014 using the shallow water Leica AHAB Chiroptera II sensor and revealed new bedrock features that were not discovered using previous mapping methods. A thick blanket of glacial till covers the bedrock on land and outcrops are exposed only along the coastal cliffs and offshore reefs. The seamless land-seabed digital elevation model (DEM) produced from the lidar survey revealed significant bedrock outcrop offshore where ocean currents have removed the glacial till, a significant finding that was hitherto hidden under the sea surface. Several reefs were identified offshore as well as a major fold structure where block faulting occurs along the limbs of the fold. The extension of the Malagash Mine Fault located ~10 km west of Cape John is proposed to explain the local folding and faulting visible in the submerged outcrops. The extension of this fault is partially visible on land where it is obscured by glacial till cover and its presence is supported by the orientation of submerged bedding and lineaments on both the south and north sides of Cape John. This paper demonstrates how near-shore high resolution topography from bathymetric lidar can be used to enhance and refine geological mapping.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".