Remote predictive mapping of surficial materials on northern Baffin Island: developing and testing techniques using Landsat TM and digital elevation data
Bibliographic record
Abstract
Considering the vastness of Nunavut, the paucity of regional-scale surficial geology maps for the territory, the significant expense of working in a remote region, and the increasing availability of affordable, remotely sensed data, it is timely to develop and test remote predictive mapping techniques for producing surficial geology maps. The goal of this remote predictive mapping project is to produce a surficial materials map, which will be used to expedite subsequent ground-based mapping and sampling. This paper describes techniques used to produce a surficial materials map for an area in northern Baffin Island using remote predictive mapping techniques with LandsatTMand digital elevation data. The predictive maps produced in advance of the field work (i.e. "ground truthing") were found to be approximately 50% accurate. To improve remote predictive mapping accuracy to at least 80%, high-resolution imagery may need to be included in the remote predictive mapping protocol.
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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 | 0.001 |
| 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) | 0.001 | 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".