Iron-oxide and alkali-calcic alteration ore systems and their polymetallic IOA, IOCG, skarn, albitite-hosted U±Au±Co, and affiliated deposits: a short course series. Part 2: overview of deposit types, distribution, ages, settings, alteration facies, and ore depositmodels
Bibliographic record
Abstract
Part 2 of the short course series reviews the deposit types, classification, distribution, ages, settings, alteration facies and ore deposit models of iron oxide and related alkali?calcic alteration ore systems. These systems include iron oxide copper?gold (IOCG) deposits and their Co? and Bi?rich variants, iron oxide-apatite (IOA) deposits and their rare?earth element?rich variants, some albitite-hosted U and Au?Co?U deposits, Mo?Re deposits and polymetallic skarn deposits. The course also discusses the continuum with epithermal systems and polymetallic vein deposits. Two main examples are used, the systems from the Great Bear magmatic zone in Canada and the Olympic Dam deposit in Australia. The main references for this chapter include Hitzman et al. (1992), Hitzman (2000), Williams et al. (2005, 2010), Oliver et al. (2006), Corriveau (2007, 2017a, b), Corriveau and Mumin (2010), Corriveau et al. (2010a, b, 2016, 2017, in press a?h), Mumin et al. (2007, 2010), Porter (2010a, b), Rusk et al. (2010), Skirrow (2010), Williams (2010a, b), Montreuil et al. (2013, 2015, 2016a, b), Ehrig et al. (2012, 2017); Barton (2014) and Richards et al. (2017).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".