Outcropping and remotely predicted lineaments, faults, fractures, and dykes in the Kiggavik uranium camp, Nunavut
Bibliographic record
Abstract
Reactivated faults and their intersections are key exploration criteria for unconformity associated uranium (U) deposits. This poster shows how lineament data were generated and compiled over the Kiggavik uranium (U) camp, Nunavut, as part of a larger goal to understand fault reactivation in and around the northeastern Thelon Basin. The remote predictive maps presented here show lineaments at various scales, from regional scale through to camp scale to highly detailed. We show how the fractal range of lineament scales can be connected to and calibrated from detailed outcrop exposures. Lineaments were added to the compilation based on single prominent, or two or more moderate but coincident, straight features and/or offsets of other linear features, such as: broad to narrow demagnetized zones, stepped changes from higher to lower aeromagnetic intensity, linear magnetic highs associated with dykes filling these faults, straight sections of streams, lake-shores and swamps, linear steps in elevation, and geological contacts. The orientations of parallel swarms of lineaments are used to assign lineaments to classes of structures consistent with Riedel shear stress models. The lineaments are interpreted as faults, fractures and shear zones that resulted from a long, episodic Proterozoic deformation history, presented herein abbreviated form. The structural geological rationales for linking the stress models to successive tectonic events are developed with outcrop examples in the simultaneously released Open File 7895. Note: References cited in poster are located in accompanying digital file.
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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 | 0.000 | 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".