Ground Truth for PaganTibet Ume models 1 & 2
Bibliographic record
Abstract
This dataset forms the triple-checked diplomatic transcriptions of the Ground Truth for the PaganTibet HTR models "Ume 1" and "Ume 2", which can be used free of charge through the Transkribus platform. This dataset is part of the PaganTibet collection, managed by Rachael Griffiths and Marieke Meelen. Transcriptions were first checked by two teams of annotators from the Triten Norbutse Institute and then double-checked by their team leaders:- Team A 'Manual input': Sherab Chokgyal (Team Leader), Tsugphud Woeser,Tsultrim Palsang, Palgyi Wangchuk, Tsewang Drukgyal- Team B 'Correction': Tsultrim Gyaltsen (Team Leader), Tritsuk Lhundup,Tsukphud Rabsal, Tsognyi Gyatso, Sherap Woser All double-checked transcriptions were then triple-checked by Rachael Griffiths, Marieke Meelen and the rest of the PaganTibet research group, led by prof Charles Ramble (PI). Accompanying images and full eTexts will be made available through the BDRC's BUDA platform. PaganTibet is an ERC-funded Advanced Grant project (ERC, PaganTibet, 101097364) led by Professor Charles Ramble(PI) in the Horizon Europe framework, hosted by the École Pratique des Hautes Études (EPHE), PSL in Paris. Copyright Rachael Griffiths et al (all listed creators), October 2025. This document is licensed under CC BY-NC-SA 4.0.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.079 |
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".