Changing our ideas about the evolution of magmatic systems with improved temporal resolution: do we get it right?
Bibliographic record
Abstract
Improvements to U-Pb geochronology of magmatic zircon have resulted in temporal resolution at the level of <0.1% for individual 206Pb/238U dates and of 0.02-0.05% for weighted mean 206Pb/238U ages of a statistically equivalent group of single crystal dates from zircon or baddeleyite (50,000 years for a Mesozoic igneous rock). This talk will give a short overview on the challenges and opportunities such high precision age determination implies in felsic and mafic magmatic systems. Felsic magmatic systems: Zircon dates from the same hand sample cover a temporal range that integrates their crystallization history in the melt. Since each grain crystalizes over a certain time period, the apparent age range is a minimum estimate of the duration of crystallization or the residence in a magma. A major challenge is the mitigation of decay-related lead loss through refined chemical abrasion procedures (Widmann et al., 2019) to avoid erroneous interpretation of zircon dates that appear too young. Apparent trace element or isotopic trends are typically not coherent with time and therefore reflect fractionation processes at different places and different times in the magmatic system, possibly within compositionally different magma batches. Mafic magmatic systems: Zircon is not a crystallizing phase in a basaltic melt, but can occur after ~90% fractionation of olivine, pyroxene and amphibole, zircon saturation can then be achieved in low-volume granitic melt pockets (depending on the water content). A zircon date is therefore an age information along the crystallization -cooling path of a mafic intrusion (Zeh et al., 2015). In low-Si and low-Zr melts, baddeleyite may arrive at saturation before zircon and can be used for dating as well. There are two clear problems with zircon/baddeleyite geochronology in mafic systems: (i) since baddeleyite saturates earlier than zircon, it should produce slightly older dates in the same rock; however, these minerals often display the inverse relationship. Since no pre-treatment for the removal of decay-damaged portions exists for baddeleyite, we can demonstrate that this discrepancy is due to lead loss. Mitigating lead loss is also difficult for zircon since it crystallized from residual melt patches of granitoid composition high in uranium, often resulting in metamict crystals; (ii) zircon populations from dolerites may spread over >100,000 years even in cases where simple thermal modeling shows that a dolerite sill has crystallized and cooled at 103 years timescales. Beside lead loss, we may suspect that certain zircon grains contain minute portions of pre-crystallization radiogenic lead from crustal contamination. We can explore and quantify cryptic inheritance through Hf, O isotopic analysis of the same dated zircon grains. Heterogeneous nucleation on relics of incompletely dissolved zircon is more probably than spontaneous nucleation. As an overarching challenge, we have no technique or independent approach to quantify lead loss and it remains the biggest uncertainty in U-Pb dating. References: Davies et al. (2015) Nature Communications, 8, 15596 ; Sell et al. (2014) Earth and Planetary Science Letters, 408, 48-56; Widmann et al. (2019) Chemical Geology, 511, 1-10; Zeh et al. (2015) Earth Planet. Sci. Lett. 418, 103-114
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.011 | 0.047 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".