On the Size and Spacing of Volcanogenic Massive Sulfide Deposits within a District with Application to the Matagami District, Quebec
Bibliographic record
Abstract
Volcanogenic massive sulfide (VMS) districts are typically ~40 km in diameter and contain about a dozen regularly spaced Zn-Cu orebodies, one or two of which contain more than half of the district’s resources. We numerically investigate this deposit size and spatial distribution by calculating zinc transport across the sea floor, first above sills of simple geometry, and then above the Bell River Complex at Matagami. For sills with simple rectangular geometry (i.e., a constant thickness), convection is strongest at the edge. The edge convection induces a progression of convection cells above the sill, although the sill cools mainly by the retreat of its thermal edges toward the sill center. If the rock permeability is a function of temperature that is maximized at 375°C, the resulting vents occur at stable, discrete locations that proxy for ore deposits. The fraction of sill heat vented at or above 300°C and the potential metal resources of the districts are greatest if the host permeability is 10−15 m2 (1 millidarcy) and the sill top is at 1 km depth. The simulations suggest that, as the host permeability increases, a single large deposit will progressively dominate a host of smaller ones. The Matagami simulations are based on a sill that tapers from 6.5 to 0 km thickness over a distance of 30 km. In the model, convection occurs both above the sill and along its underside, and metal is extracted from both sides of the cooling sill. The spacing of the resulting discharge sites is similar to that observed in actual VMS districts and, where direct comparison is possible, the mass of metal deposited is similar for an accumulation efficiency of ~3 percent.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".