An evolutionary model and classification scheme for nephrite jade based on veining, fabric development, and the role of dissolution–precipitation
Bibliographic record
Abstract
Although nephrite jade has been collected and treasured since the Stone Age, we lack a clear understanding of how it forms during deformation and metasomatism in shear zones. Using microstructural analysis of samples from Taiwan, California, and New Zealand, we propose a conceptual model for the evolution of nephrite jade that distinguishes four nephrite types based on mode of formation and textural characteristics: (1) primary (type 1a) or folded (type 1b) vein nephrite, (2) crenulated nephrite (type 2), (3) foliated semi-nephrite (type 3), and (4) nodular or domainal nephrite (type 4). We interpret the texture of our analysed samples to represent snapshots of a progressive textural evolution similar to that experienced by other deformed and fine-grained metamorphic rocks that develop under fluid-present, greenschist-facies conditions. Our observations suggest that types 2 and 3 nephrite can evolve from vein nephrite (type 1) by the development of crenulated and foliated metamorphic fabrics, during which the most important deformation process is dissolution-precipitation. However, development of metamorphic fabrics can be interrupted by transient brittle deformation, leading to the formation of type 4 nephrite that is characterised by nodular or angular clasts of nephrite in a nephritic matrix.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".