Low-Ca Garnet Harzburgite Xenoliths from Southern Africa: Abundance, Composition, and Bearing on the Structure and Evolution of the Subcratonic Lithosphere
Bibliographic record
Abstract
Most natural diamonds probably exist in the upper mantle as members of a low-Ca garnet harzburgite assemblage.Xenoliths of low-Ca garnet harzburgites (with or without diamonds) are purported to be rare, although xenocrysts of low-Ca Cr-pyrope derived from such rocks have been shown to exist in virtually all kimberlites on the Kaapvaal Craton in southern Africa (e.g., Boyd and Gurney, 1982; Gurney, 1985).This has led to suggestions that, relative to other types of mantle xenoliths, low-Ca garnet harzburgites disaggregate more readily upon eruption, yielding xenocrysts of diamond and low-Ca garnet, with few intact low-Ca garnet harzburgite xenoliths surviving (e.g., Boyd and Gurney, 1982; Gurney, 1985).In the present study, xenoliths of low-Ca garnet harzburgite were sought in the Kimberley dumps, and their abundance compared with estimates from garnet xenocryst populations of the Kimberley mines.Investigation of garnet xenocrysts was extended to include 11 additional kimberlites across the Kaapvaal Craton.Note that in similar, earlier studies only Cr-rich purple garnets were analyzed, and thus the data cannot be used to estimate the abundance of low-Ca garnet harzburgites in the upper mantle.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".