Bibliographic record
Abstract
Diamonds have been discovered in a variety of potassic ultramafic rocks including group-I and group-II kimberlites, olivine lamproites and aillikites, all of which are macroscopically similar and can be difficult to differentiate when viewed under the microscope. However, group-I kimberlites, and to a much lesser extent group-II kimberlites and olivine lamproites, are known to contain economic concentrations of diamonds. This study addresses the problem of distinguishing among different types of kimberlitic and related rocks by developing a geochemically-based method for classifying them. Geochemical methods have been largely ignored in the classification of kimberlites and related rock types due to high concentrations of xenoliths. However, this problem can be largely overcome by only selecting matrix material for analysis. An evolving kimberlitic magma will become enriched or improvished in Si due to the fractionation of olivine and phlogopite, depending on the initial Si concentration of the magma. As they have low Si concentrations, group-I kimberlites and aillikites can be separated from group-II kimberlites and meimechites, which have higher Si concentrations for any Mg content. Furthermore, since aillikites and meimechites are relatively rich in Fe compared to group-I and group-II kimberlites, these rock types form four separate fields on a Si vs. Fe discrimination diagram. Similar rock-type separation is observed when the ratio of La to Yb is plotted against the ratio of Sm to Yb. Kimberlite and other potassic ultramafic rocks were sampled from nine areas in Quebec: the Otish Mountains, Wemindji, Torngat Mountains, Desmaraisville, Temiscamingue, Ile Bizard, Lac Leclair, Baie James and Ayer's Cliff regions. Major and selected trace element concentrations were determined by XRF analysis for all samples, while a subset of representative samples was selected for trace element analysis by ICP-MS. Electron microprobe analyses of unaltered olivine and phlogopite were also conducted. Of the 37 samples that were classified both mineralogically and chemically, 23 or 62% were correctly classified using Fe and Si. This number increases to 84%, if the REE are used in conjunction with Si and Fe. The Si vs. Fe discrimination diagram separates group-I kimberlite from most aillikite and meimechite rocks and group-II kimberlite/olivine lamproite rocks from most aillikite and meimechite rocks. Therefore, major and trace element geochemistry offers an important tool for the classification of kimberlitic rocks. Vasilenko et al. (2002) and Francis (2003) both suggested that diamond grades can be correlated with the major element compositions of the kimberlites. The data collected in this study confirm the inverse relationship between TiO2 concentration and diamond grade. The lowest TiO 2 values were obtained on samples from the Otish Mountains and Renard samples in particular. Other areas of Quebec are characterized by higher TiO2 contents with most samples containing greater than 2 wt% TiO 2. Therefore, the kimberlitic rocks from the Renard locality have the greatest potential for an economic diamond deposit. The origin of this correlation needs to be explored, however, because it is unclear whether this is a feature of the mantle source, or reflects the survivability of diamonds within the kimberlites.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.007 | 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".