Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> Plots of daughter against parent concentration (D-P plots) are widely used as isotope ratio plots in geochronology. Their main purposes are: (1) to visualize the main ingredient of the radiometric age equation – the daughter-parent ratio – and (2) to inspect the daughter-parent relationship for anomalous behavior indicating influences of geological processes or analytical bias. Despite their benefits, D-P plots are currently not used for analyzing low-temperature thermochronology data. This contribution aims at putting D-P plots on the map as a data analysis tool. We present a simple, decision-tree-based classification for daughter-parent relationships that places a dataset into one of seven classes: linear relationship with zero intercept, cluster, linear relationship with systematic offset, non-linear relationship, several age populations, scattered data, and inverse relationship. Assigning a class to a dataset enables to choose further data analysis steps and the right algorithm to calculate a sample age, e.g. as pooled, central or isochron age, or a range of ages. We discuss how to deal with small sample sizes and the possibility of comparing data across samples and chronometers. Our simple classification scheme uses the information in the D-P plot for facilitating thermochronological data analysis and making it more consistent and traceable.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".