Dealing with discordance: a novel approach for analysing U–Pb detrital zircon datasets
Bibliographic record
Abstract
Detrital zircon U–Pb geochronology is a rapidly expanding and useful technique for addressing the sedimentary rock record. However, because of difficulties in evaluating discordant analyses, common in most datasets, a high proportion of results may be discarded. These analyses, if interpreted correctly, can provide valuable information regarding the deformation, alteration or metamorphism of the zircon population, as these events are all capable of producing U–Pb discordance. A novel modelling procedure permits analysis of probabilistic relationships within U–Pb datasets to deconvolve the age information present within discordant analyses by assessing the relative likelihood of potential discordia lines. Additionally, because we retrieve useful information from discordant data, a stricter filter can be used to assess concordance, increasing confidence in those distributions. The validity of this modelling method is demonstrated using two previously published cases from the Caledonide orogen where clearly discordant analyses exist. In the Southern Uplands of Scotland, these analyses indicate resetting of the U–Pb systematics in metasedimentary rocks in the Grampian orogen prior to Ordovician erosion and redeposition. In the second case, from the Greenland Caledonides, discordant data provide additional constraints on previously proposed in situ resetting during Scandian deformation and fluid flow events. Supplementary material: The code used for writing the model, along with an instruction guide, is available at http://doi.org/10.6084/m9.figshare.c.2182197 .
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".