The daughter–parent plot: a tool for analyzing thermochronological data
Bibliographic record
Abstract
Data plots of daughter against parent concentration (D–P plots) are a potential tool for analyzing low-temperature thermochronology, similar to isochron plots in radioisotopic geochronology. Their purposes are to visualize the main term of the radiometric age equation – the daughter–parent ratio – and to inspect the daughter–parent relationship for anomalies indicating influences of geological processes or analytical bias. The main advantages of the D–P plot over other data analysis tools are (1) its ability to detect systematic offsets in D and P concentrations, (2) its unambiguous representation of radiation-damage-dependent daughter retention, and (3) the possibility to analyze potential age outliers. Despite these benefits, the D–P plot is currently not used for analyzing low-temperature thermochronology data, e.g., from fission-track, (U–Th) / He, or zircon Raman dating. We present a simple, decision-tree-based classification for daughter–parent relationships based on the D–P plot that places a dataset into one of seven classes: linear relationship with zero intercept, cluster, linear relationship with systematic offset, nonlinear relationship, several age populations, scattered data, and inverse relationship. Assigning a class to a dataset enables choosing further data analysis steps and how to report a sample age, e.g., as a pooled, central, or isochron age or a range of ages. This classification scheme aims at facilitating thermochronological data analysis and making decisions more transparent. We demonstrate the proposed procedure by analyzing published datasets from a variety of geological settings and thermochronometers and introduce Incaplot, which is graphical user interface software that we developed to facilitate D–P plotting of thermochronology data.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.017 |
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".