Current practices in building and reporting age-depth models
Bibliographic record
Abstract
ABSTRACT Age-depth models provide essential temporal frameworks in paleoenvironmental science. We use a sample of 80 recently-published age-depth models to comment on current practices in building and reporting radiocarbon-based age-depth models. We address options for model building, sampling strategies, dating densities, and best practices for reporting age-depth models and associated data. Our review reveals incomplete reporting of14C ages, model-building methods, age-depth models and associated meta-data in many recent studies. All information needed to evaluate, reproduce and update an age-depth model should accompany every published model. We also present a case study of building age-depth models for a lake sediment core that has both14C ages and an independent varve chronology. The case study illustrates that choosing the ‘best model’ is not a simple task, and that model accuracy is ultimately controlled by differences between14C ages and true age that likely occur in many late Quaternary records.
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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 | 0.258 | 0.616 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.014 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier 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".