Bibliographic record
Abstract
The wish to obtain age data from garnet became reality in 1980 when Griffin and Brueckner1 published the first-ever Sm-Nd garnet ages. The Silurian Sm-Nd ages that they obtained for eclogites, which until then had been considered Precambrian in age, showed the tremendous potential of this technique. Following analytical developments and constraints on chronometer systematics2, the technique became widely used in various metamorphic systems and settings, including in rocks from the upper mantle. Following the first successful attempt at Lu-Hf garnet dating3 and calibration of the 176Lu decay constant4,5, the Lu-Hf system was established as an alternative to Sm-Nd in garnet chronology, with possible advantages, including higher P/D, shorter half-life and lower daughter-element diffusivity.Like other radiometric techniques that use isotope dilution, these techniques have the advantage of high precision, but are time-intensive and provide grain-averaged ages. Dating of individual garnet zones with either technique is possible, but only in specific cases6-8. Novel approaches to in-situ garnet chronology by combining laser ablation micro-sampling with U-Pb analysis by MC-ICP-MS9 or Lu-Hf analysis by ICP-MS/MS10 provide an exciting addition to garnet chronology, with advantages and disadvantages inverted compared to convention techniques: lower precision, but rapid throughput and high spatial resolution.The field of garnet chronology is now at an exciting point, where applications of the technique have greatly diversified, new techniques are emerging, and improvements are ongoing – in sample-size requirements for conventional techniques, and accuracy and precision for in-situ techniques. How do these techniques compare, and which approach is best, or "good enough", in a given case? This presentation will focus on the status-quo anno 2023, and will explore frontier applications of Lu-Hf garnet dating in the crust and mantle.1 Griffin, W.L., Brueckner, H.K. (1980) Nature 285, 319-321.2 Mezger, K., Essene, E.J., Halliday, A.N. (1992) Earth Planet. Sci. Lett. 113, 397-409.3 Duchêne, S., Blichert-Toft, J., Luais, B., Télouk, P., Lardeaux, J.-M., Albarède, F. (1997) Nature 387, 586-589.4 Scherer, E.E., Münker, C., Mezger, K. (2001) Science 293, 683-687.5 Söderlund, U., Patchett, P.J., Vervoort, J.D., Isachsen, C.E. (2004) Earth Planet. Sci. Lett. 219, 311-324.6 Pollington, A.D., Baxter, E.F., (2010) Earth Planet. Sci. Lett. 293, 63-71.7 Dragovic, B., Baxter, E.F., Caddick, M.J. (2015) Earth Planet. Sci. Lett. 413, 111-122.8 Tual, L., Smit, M.A., Cutts, J.A., Kooijman, E., Kielman-Schmitt, M., Majka, J., Foulds, I. (2022) Chem. Geol. 607, 121003.9 Millonig, L.J., Albert, R., Gerdes, A., Avigad, D., Dietsch, C. (2020) Earth Planet. Sci. Lett. 552, 116589.10 Simpson, A., Gilbert, S., Tamblyn, R., Hand, M., Spandler, C., Gillespie, J., Nixon, A., Glorie, S. (2021) Chem. Geol. 577, 120299.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".