Identifying kimberlite indicator mineral dispersal trains in the Pelly Bay region, Nunavut, Canada using GIS interpolation
Bibliographic record
Abstract
ABSTRACT Identification of kimberlite indicator mineral dispersal trains in glaciated terrain provides important clues in determining provenance. Complex ice flow history can distribute kimberlite indicator minerals such that dispersal trains are disguised within clusters of till samples containing abundant kimberlite indicator minerals. This study uses GIS with inverse distance weighted interpolation to identify and isolate dispersal trains within areas where large clusters of till samples with abundant kimberlite indicator minerals exhibit no apparent distribution patterns. The method was tested in the Pelly Bay region of Nunavut, Canada, where many areas contain clusters of till samples with abundant kimberlite indicator minerals, the method delineated dispersal trains within these areas. The study identified trains ranging from 1.5–7 km in length, with the heads of some trains ranging in width between 225 m and 3 km. Mg-ilmenite is the most abundant kimberlite indicator mineral in the Pelly Bay area, however, several trains with distinct relative abundances of kimberlite indicator mineral species were identified that suggest the presence of kimberlites with different intrusive phases. Our study suggests that several of the identified dispersal trains likely originated from kimberlite dykes and/or sills occupying NW–SE oriented structures in the bedrock.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".