Rare metal indicator minerals in bedrock and till at the Strange Lake peralkaline complex, Quebec and Labrador, Canada
Bibliographic record
Abstract
A study of rare metal indicator minerals and glacial dispersal was carried out at the Strange Lake Zr – Y – heavy rare earth element deposit in northern Quebec and Labrador, Canada. The heavy mineral (>3.2 specific gravity) and mid-density (3.0–3.2 specific gravity) nonferromagnetic fractions of mineralized bedrock from the deposit and till up to 50 km down ice of the deposit were examined to determine the potential of using rare earth element and high fileld strength element indicator minerals for exploration. The deposit contains oxide, silicate, phosphate, and carbonate indicator minerals, some of which (cerianite, uraninite, fluorapatite, rhabdophane, thorianite, danburite, and aeschynite) have not been reported in previous bedrock studies of Strange Lake. Indicator minerals that could be useful in the exploration for similar deposits include Zr silicates (zircon, secondary gittinsite (CaZrSi2O7), and other hydrated Zr±Y±Ca silicates), pyrochlore ((Na,Ca)2Nb2O6(OH,F)), and thorite (Th(SiO4))/thorianite (ThO2) as well as rare earth element minerals monazite ((La,Ce,Y,Th)PO4), chevkinite ((Ce,La,Ca,Th)4(Fe,Mg)2(Ti,Fe)3Si4O22), parisite (Ca(Ce,La)2(CO3)3F2), bastnaesite (Ce(CO3)F), kainosite (Ca2(Y,Ce)2Si4O12(CO3)·H2O), and allanite ((Ce,Ca,Y)2(Al,Fe)3(SiO4)3(OH)). Rare metal indicator minerals can be added to the expanding list of indicator minerals that can be recovered from surficial sediments and used to explore for a broad range of deposit types and commodities that already include diamonds and precious, base, and strategic metals.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".