SI_Table_S1_unmodeled_modeled_Bayesian_results_GravettoNiches.tab
Bibliographic record
Abstract
List of 14C ages, and their associated archaeological contexts, analyzed with the two Bayesian age model structures described in the text. The ages are organized by archaeological cultural phase. The unmodeled (calibrated) Highest Posterior Distribution (HPD, 95%) are provided in columns H and I. Columns J and K contain the results of Banks et al.’s [1] model run with the IntCal20 calibration curve [65]. Columns L and M contain the posterior intervals for event dates and cultural phases employing the same model structure as Banks et al. [1] including newly published ages as well as those obtained in this study. Columns N and O present posterior intervals for event dates and cultural phases obtained with an age model that separates the Northern Noaillian and Rayssian cultures of the Middle Gravettian into distinct, successive phases. Outlier determinations were made by examining the median (Q2) posterior standard deviations for the individual event dates and those with values greater than 420 are considered outliers. Outliers were removed from the models employing the 2019 model structure. Since outliers are heavily penalized by ChronoModel, their removal was not necessary for the second model structure that separated the Northern Noaillian and Rayssian into successive phases. Event dates with median posterior standard deviations greater than 420 are depicted in bold, maroon italics in columns N and O.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.379 | 0.182 |
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".