Exploring Earth’s Deep Water Cycle using Sublithospheric Diamonds
Bibliographic record
Abstract
Sublithospheric diamonds, which may form within the transition zone and lower mantle, are the very deepest direct samples of the Earth's interior.[e.g., 1, 2, 3].Diamond can protect mineral inclusions from reactions during ascent to the surface.Therefore, the study of inclusions in diamond often provides invaluable insight into the geochemical and physical conditions of their formation and more broadly planetary volatile cycling [4].Combining synchrotron X-ray computed microtomography and X-ray diffraction at the GSECARS sector of the Advanced Photon Source, we are analyzing mineral inclusions in-situ within a suite of about fifty diamonds from a known super-deep diamond locality in Juina, Brazil.Pink beam microtomography first enables high resolution mapping (1.24 microns/pixel) of mineral inclusions within host diamonds.The tomography data also reveal microcracks within the diamonds that may lead to secondary alteration of their inclusions.After locating pristine inclusions of interest, we employ single-crystal and powder Xray diffraction to identify individual inclusions.The primary objective of this research is to identify and characterize silicate inclusions within these diamonds and determine their degree of hydration to understand Earth's deep water cycle.We will present the results of our mineral inclusion work to date, including the range of mineral phases identified, their likely origin, and their implications for deep Earth composition and dynamics.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".