Application of Hyperspectral Data for Remote Predictive Mapping, Baffin Island, Canada
Bibliographic record
Abstract
Abstract This study demonstrates the application of airborne hyperspectral data for the generation of accurate remote predictive geologic maps, which can be used to assist regional mapping by (1) giving detailed spatial and spectral information, (2) focusing future mapping projects, and (3) highlighting areas of economic potential. The study area is located in southern Baffin Island and comprises a diverse assemblage of lithologic units that are part of the northeastern segment of the Paleoproterozoic Trans-Hudson orogen. Two steps were required to generate remote predictive geologic maps from the hyperspectral image: the extraction of image end members and the application of spectral mixture analysis to generate fractional abundance maps; and converting the fractional abundance maps into predictive geologic maps. Eleven geologic units were extracted from the image data as end members. These end members were identified based on characteristic spectral features and comparisons with field and laboratory spectra. The predictive map correlates well with the existing published map, but more extensive exposures of potentially economic peridotite and carbonate units were found. Lichen-rock mixtures were used to map quartzofeldspathic units that are covered by thick lichen coatings in this region.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".