Chapter 6: Mechanical Regimes of Hydrothermal Gold Mineralization
Bibliographic record
Abstract
Abstract Pore fluid pressure and differential stress are among the most important controls on the mechanical behavior of mineralizing systems. Their separate influences can be readily identified on failure mode diagrams, which show failure envelopes for pore fluid factor or pore fluid pressure at failure against differential stress. The effect of the intermediate principal stress can be shown on such diagrams by using a failure criterion that includes all three principal stresses, such as the Murrell extension to the Griffith criterion. The effect is apparent from the significant variation of the position of the failure envelope as a function of the ratio between the three principal stresses, which is therefore another important control on failure. Characteristic regimes for different gold deposit types occur as distinctive fields on failure mode diagrams. Carlin, epithermal, and volcanogenic massive sulfide (VMS) deposit types have low absolute pore fluid pressures. Iron oxide copper-gold (IOCG), intrusion-related gold, and porphyry deposits encompass low to intermediate values of pore fluid pressure, while the field of lode gold deposits may extend to the highest pore fluid pressures. Lode gold, IOCG, and epithermal deposit types may have the largest values of differential stress. Carlin and VMS deposits are associated with normal stress regimes; the other deposit types may have structures that that formed in either normal or reverse stress regimes. Exploring the effects of the stress ratio and refining these currently broadly defined regimes for the mechanics of mineralization are important future directions for research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".