Decoding 3-D monazite textures using LA-ICP-MS raster mapping
Bibliographic record
Abstract
Texturally complex monazite grains within two granulite-facies pelitic migmatites from southern Baffin Island, Arctic Canada, were mapped by laser ablation-inductively coupled plasma-mass spectrometry to quantitatively determine the spatial variation in trace element chemistry with a 4-5 μm resolution (with up to 1883 analyses per grain). The maps demarcate growth zones, some of which were cryptic with conventional imaging, highlighting the 3-D complexity of monazite grains that have experienced multiple episodes of growth and resorption during high-grade metamorphism. Associated monazite trace element systematics are highly variable, both within domains interpreted to have grown in a single event, and between samples that experienced similar metamorphic conditions and mineral assemblages. This result cautions against generalised petrological interpretations being made about monazite trace element signatures as it suggests sample-specific controls. Nevertheless, by quantifying monazite textures, a related U-Pb dataset is re-interpreted, allowing ages to be extracted from a continuum of concordant data. The results reveal a ~45 Myr interval between prograde metamorphism and retrograde melt crystallisation in the study region, emphasising the long-lived nature of heat flow in high-grade metamorphic terranes. Careful characterisation of monazite grains suggests that continuum-style U-Pb datasets can be decoded to provide insights into the rates of metamorphic processes.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".