PHYSICAL MODELING OF THE FORMATION OF KOMATIITE-HOSTED NICKEL DEPOSITS AND A REVIEW OF THE THERMAL EROSION PARADIGM
Bibliographic record
Abstract
Theoretical assessment of the physics of komatiites as resulting from open-channel flows indicates them to be turbulent and to have velocities ranging from meters/second to tens of meters per second, depending on slope and thickness ( i.e. , depth) of the flow. These flows should also carry stratifications of suspended load (due to increasing content of crystals as the melt cools along its run) as do rivers transporting sediment. Depending on the distance from the source, the bed load may be rich in olivine crystals, the suspended load above it, rich in sulfides, and the upper portions of the flow may consist of more evolved material. Calculated thicknesses of these suspended loads and their vertical distributions correspond to field observations. Primocrysts ( e.g. , chromian spinel) should accumulate at the boundaries separating each suspended load in the flow. The observed drop in temperature per kilometer of run of basalt lava flows provides a means to imply cooling rates for higher-temperature komatiite flows. Barring supersaturation, the implied rates of cooling indicate that sulfides should begin to precipitate from a komatiitic melt around 40 km from the source. Finite-element modeling of these flows indicates that eddies form on the upstream side of ledges or in hollows. These eddies provide an extremely efficient means for scavenging metals into sulfides, and their morphology corresponds to massive sulfide deposits associated with embayments. The vigor of the eddies is dependent on the Reynolds number Re , and indicates Re to be as important as the R factor for the formation of economic deposits. The sulfides must be well mixed with the melt in order to scavenge the greatest amount of nickel. Scavenging efficacy increases with Re to a maximum, then decreases. For example, a thick flow (large R factor) will be fast (large Reynolds number), in which case the melt may simply hurdle the embayment with little eddy formation and little catchment for massive sulfide formation. These numerical models support some of the physical models of Naldrett and coworkers. It is difficult to incorporate the results here into thermal erosion models, which is in line with recent critiques of such mechanisms. Thermal erosion is not seen in industrial practice, where continuous casting of melts of temperatures considerably higher than magmas yield only chilled margins between sluice and melt, which armors against any chemical communication between the two. Similarly, Hawaiian lavas do not thermally erode tar roads. A quenched crust is formed over the road, which armors it against the lava above it. The results herein seem to indicate the necessity of some topography to “trip” the flow and get it tumbling in order to secure thorough mixing.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".