Modelling Seismically Induced Mesothermal Goldfields along the Deep-Rooted Cadillac-Larder Lake Fault, Abitibi, Canada
Bibliographic record
Abstract
Gold deposits are not uniformly distributed along major faults due to complex (and long-debated) interactions between seismicity, hydrothermalism, and structural heterogeneities. Here, we use static stress modelling (SSM) to quantitatively investigate these interactions, by exploring the role of Cadillac-Larder Lake Fault (CLLF) Archean seismicity in the genesis of the regional goldfields. Various rheological factors are evaluated for optimizing the models’ ability to reproduce known gold occurrences, regarded as the fossil primary markers of synkinematic hydrothermal systems. We propose that the marked structural heterogeneities of the CLLF induced persistent seismic segmentation and recurrent ruptures of the same fault windows that arrested on robust node points. These ruptures favour repeated occurrences of seismically triggered hydrothermalism along long-existing fluid pathways having an enhanced permeability and iterative ore formation into supracrustal discharge zones by means of episodic drops and build-ups of pressure. Two-dimensional SSM permits the predictive mapping of these high-potential zones. These modelled zones correlate positively with the actual observed gold distribution. We demonstrate that (1) the ruptures along the Joannes Segment arresting on the Davidson Fault and Lapa’s bend can explain the occurrence and location of the Rouyn and Malartic goldfields; (2) the models’ validity is improved by implementing regional geological constraints; and (3) the distant gold occurrences from the CLLF, including the Bourlamaque field, can be explained by doublet seismic events along the Rivière-Héva and Lapause subsidiary faults. Our results provide new perspectives from a fundamental standpoint and for exploration purposes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".