Concepts and revised models for Phanerozoic orogenic gold deposits
Bibliographic record
Abstract
Abstract Existing published models for orogenic gold deposits (OGDs) do not adequately describe or explain most deposits of Phanerozoic age, and there are numerous reasons why Phanerozoic OGDs might differ significantly from older deposits. We subdivide Phanerozoic OGDs into four main subtypes, based on a number of descriptive criteria, including tectonic setting, lithological siting, and characteristics of the mineralization in each subtype. The four subtypes are: (1) crustal-scale fault-associated (CSF) subtype, (2) sediment-hosted orogenic gold (SHOG) subtype, (3) forearc (FA) subtype, and (4) syn- and late tectonic dispersed (SLTD) subtype. Lead isotopic studies suggest that Pb and other metals in all but the FA subtype were likely derived from relatively small source reservoirs in the middle or upper crust. OGDs formed in large, lithologically and structurally homogeneous regions will tend to be of the same subtype; however, in geologically complex orogenic belts it is common to find two or more subtypes that formed at approximately the same time. Based on the synthesis of global OGDs of Phanerozoic age, districts containing CSF or SHOG subtype deposits appear to have the best potential for hosting multiple large deposits. FA subtype deposits form in a relatively uncommon tectonic setting (accretionary forearc, possibly overlying a subducting spreading ridge) and are likely to be rare. SLTD subtype OGDs are the most common, but most are small and uneconomic, although they commonly generate substantial alluvial gold deposits.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".