Remote Predictive Mapping 4. Utilizing High Resolution Satellite Imagery, Western Minto Inlier, Victoria Island, NWT
Bibliographic record
Abstract
The very high spatial resolution and stereo capability of GeoEye-1 images were utilized to map the geology of a part of the western Neoproterozoic Minto Inlier on Victoria Island. To optimize the results of predictive mapping, a LANDSAT-7 image together with a SPOT-5 image were also used in concert with the GeoEye-1 images. The predictive bedrock geology map, interpreted based on 3D stereo visualization, presents much more detailed geological information compared to the existing 1:500,000 scale geological map of the area. The high spatial and moderate spectral resolution of GeoEye images allowed us to distinguish a black shale unit (black shale member), and resolve subtle spectral and textural differences between massive stromatolitic dolostone and dolostone containing fine-grained interlayers in an upper carbonate member. As well, an important distinction could be made between Proterozoic sedimentary strata and unconformably overlying interlayered sandstone and carbonate rocks of Cambro-Ordovician age. The SWIR bands in the LANDSAT and SPOT images proved to be very useful in identifying gabbro sills. A geological map, based on field work, was used to evaluate the remote predictive map. Comparison of the predictive map with the field map shows that the two maps look similar in terms of the regional distribution of the lithological units; however, there are discrepancies between the two maps, especially in areas in which the bedrock is covered by glacial sediments and/or other overburden materials. The spectral similarity between different stratigraphic units comprising similar rock types, also contributed to differences between the predictive map and the field map.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".