Bibliographic record
Abstract
Garnet chronology became a reality in 1980 when the firstever Sm-Nd garnet ages were published [1] .Following the first Lu-Hf garnet ages -coincidentally out of Lyon [2] -and calibration of the 176 Lu decay constant [3,4] , the Lu-Hf system soon emerged as a powerful alternative to Sm-Nd with following advantages: generally higher parent/daughter ratios, shorter halflife and lower daughter-element diffusivity [5] .The technique now represents a central means of dating petrological and tectonic processes in cratons, subduction zones and orogenic belts.Like other radiometric techniques that use isotope dilution, Sm-Nd and Lu-Hf chronology have the advantage of high precision, but the disadvantage of limited spatial resolution.Dating of individual garnet zones with is possible, but only in specific cases [6][7][8] .Novel in-situ U-Pb [9] and Lu-Hf [10] garnet chronology provides an exciting new addition to the field, with "flipped" advantages and disadvantages: lower precision, but rapid throughput and high spatial resolution.Garnet chronology is at a very exciting stage of development, with applications diversifying, new techniques emerging, insights into chronometer systematics improving, and the analytical gap between ID and in-situ techniques narrowing.Combined with trace-element mapping, garnet chronology now provides unprecedented insight into how, when and how fast garnet grows in metamorphic and magmatic rocks.So where are we exactly and where are we headed?In this presentation, I will discuss the status-quo anno 2023, and will explore frontier applications of Lu-Hf garnet chronology in dating petrological processes in the crust and cratonic mantle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".