Preliminary geochemical characterization of the Central Mineral Belt uranium geochemistry database
Bibliographic record
Abstract
Preliminary geochemical characterization of the U±Cu±Mo±V mineralization in the Central Mineral Belt (CMB) of Labrador is developed based on data compiled within the recently published Central Mineral Belt uranium geochemistry database. The highest uranium concentrations are found in the Jacques Lake (up to 11 weight % U) and Michel-in (up to 2 weight % U) areas, whereas copper and molybdenum concentrations are the highest in the Moran Lake (up to 1.6 weight % Cu) and Jacques (up to 1 weight % Mo) and Anna Lake (up to 0.3 weight % Mo) areas, respectively. Sodic alteration is the most common alkali alteration type in the CMB, with the emplacement of iron oxides mainly decoupled from potassium. Uranium-rich samples also plot within the 'least altered' field in the alteration type discriminant diagram. However, this might be an artefact of distinct alteration type overlaps, which may shift the major element composition of the mineralized rocks towards the least altered field. The lack of association of uranium and other base metals with alkali elements is further recognized by principal component analysis. However, additional statistical evaluation focused on the individual alteration types and categorized by the geographical areas within the CMB is necessary to better understand the mobility of uranium and base metal elements, along with their association with alteration facies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".