Quantifying hydrothermal alteration with normative minerals and other chemical tools at the Beattie Syenite, Abitibi greenstone belt, Canada
Bibliographic record
Abstract
Methods to quantify hydrothermal alteration are used to: document alteration halos; to comprehend hydrothermal processes; and to prospect for mineralisation formed by such processes. The chemical methods available are numerous and each has specific advantages, limitations and fields of application. This study focuses on the hydrothermal process at Beattie, a Neoarchean gold deposit hosted by the Beattie Syenite within the Abitibi Greenstone Belt, Superior Province, Canada. To quantify alteration, it was necessary to use the following chemical methods: (1) mass-balance calculations; (2) normative minerals and related alteration indices; and (3) Pearce Element Ratios (PER) diagrams. In the study area, silicification and carbonatisation are satisfactorily quantified by alteration indices, while alkali metasomatism was best estimated by mass-balance calculations and PER diagrams. Combining these methods, the following alteration types have been documented: K-feldspar alteration, silicification, and Na-Ca-leaching are intense and proximal to gold mineralisation; carbonatisation is widespread and intense; and sericitisation and chloritisation are minor to absent. It is proposed that, at the Beattie Syenite, the formation of white mica and chlorite and the mobilisation of alkali and silica are consequences of the predominant process related to carbonatisation.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".