Mapping of glacial erratic boulders for mineral exploration: A review with LiDAR-based examples from modern and ancient glaciated terrains
Bibliographic record
Abstract
Mineral exploration in glaciated terrains is dependent on a thorough understanding of the history and mode of flow of Pleistocene mid-latitude ice sheets to successfully identify potential mineralized targets in bedrock partially or completely covered by glacial sediments. Mapping of glaciated terrains and exploration using boulder trains and mineralized dispersal trains on the glaciated crystalline shields of Canada and Fennoscandia is challenged by vegetative covers and remote sensing data of inadequate resolution. Two methodological approaches for processing and visualizing drone mapped high-resolution LiDAR data, are described herein and used to identify and map erratic boulders at 1) a modern glacier foreland (Saskatchewan Glacier) in Alberta, where an extensive boulder-strewn and partially drumlinized till surface free of vegetative cover, is being exposed by ice retreat since 1854; and 2) at an area of known lithium and tin mineralisation obscured by thick forest cover in Nova Scotia, which has been affected by several Pleistocene glaciations, most recently by the Appalachian Glacier Complex some 20,000 years ago. The first methodological approach (Strip Alignment) is used to correct offsets formed between adjacent drone flight paths to enhance data coverage and resolution. The second (Semi-Automated Tree Point Classification) is used to eliminate vegetative covers from LiDAR point clouds. In combination with statistical analysis (Point Density), these methods permit successful mapping of erratic boulders and their spatial density. This approach can now be scaled up for regional mineral exploration projects and for geomorphic mapping of modern and ancient glaciated terrains to determine former ice flow trajectories and landform evolution.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".