Remote predictive mapping of surficial earth materials: Wager Bay North area, Nunavut - NTS 46-E (N), 46-K (SW), 46-L, 46-M (SW), 56-H (N), 56-I and 56-J (S)
Bibliographic record
Abstract
Remote predictive mapping (RPM) of surficial earth materials in the Wager Bay North Area [NTS 046E (N), 046K (SW), 046L, 046M (SW), 056H (N), 056I and 056J (S)] was undertaken as part of the Geo-mapping for Energy and Minerals (GEM) Melville Peninsula Multiple Metals Project. A mosaic comprising seven separate LANDSAT TM 7 images was prepared for the classification of surficial materials. Training areas representative of 12 surficial material classes were identified through the use of airphoto interpretation, LANDSAT imagery and field knowledge of the mapping area. The statistical separability of the training areas with respect to spectral reflectance was evaluated using transformed divergence analysis. The Robust Classification Method (RCM) was used to classify the LANDSAT imagery producing a number of predictive maps of surficial materials. These maps were statistically analysed via a confusion matrix and associated measures of accuracy, then geologically evaluated through the use of airphotos in concert with field observations. The mapping of surficial materials using LANDSAT data is not without problems but does generate useful predictive maps that serve to focus and guide more detailed field mapping studies as well as providing information on surficial materials in extensive areas that cannot be field mapped. Incorporation of field knowledge and the expertise of Quaternary geologists are critical to the production of predictive maps of surficial materials through the entire remote predictive mapping process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".