Bibliographic record
Abstract
Version 0.1.0 What's Changed (Application) Début de page pour générer les éléments YAML pour les p… by @PonteIneptique in https://github.com/HTR-United/htr-united/pull/6 Update sur README by @alix-tz in https://github.com/HTR-United/htr-united/pull/11 adding a yml by @alix-tz in https://github.com/HTR-United/htr-united/pull/12 Adding dahncorpus by @FloChiff in https://github.com/HTR-United/htr-united/pull/15 Create CITATION.CFF by @PonteIneptique in https://github.com/HTR-United/htr-united/pull/18 Add catalog folder by @PonteIneptique in https://github.com/HTR-United/htr-united/pull/20 Create ocr17plus.yml by @alix-tz in https://github.com/HTR-United/htr-united/pull/25 Adding description of genauto-td-htr dataset by @alix-tz in https://github.com/HTR-United/htr-united/pull/24 Update catalog files to be compliant with the latest schema by @PonteIneptique in https://github.com/HTR-United/htr-united/pull/31 Create HTRUC by @PonteIneptique in https://github.com/HTR-United/htr-united/pull/34 Update ocr17plus.yml by @PonteIneptique in https://github.com/HTR-United/htr-united/pull/37 Template FR ajout d'un nouveau dataset by @alix-tz in https://github.com/HTR-United/htr-united/pull/38 ajout template depot dans catalog by @alix-tz in https://github.com/HTR-United/htr-united/pull/28 Ajout Paris Time Machine - Adresses et Annuaires by @PonteIneptique in https://github.com/HTR-United/htr-united/pull/35 Create Catalog.yaml by @PonteIneptique in https://github.com/HTR-United/htr-united/pull/40 Update Catalog.yaml by @PonteIneptique in https://github.com/HTR-United/htr-united/pull/41 Addition of the RASAM Dataset by @CVidalG in https://github.com/HTR-United/htr-united/pull/44 Création d'une charte de réutilisation by @alix-tz in https://github.com/HTR-United/htr-united/pull/45 WIP: Update README.md by @alix-tz in https://github.com/HTR-United/htr-united/pull/47 Update LICENSE by @alix-tz in https://github.com/HTR-United/htr-united/pull/49 add new shakespeare-scott translation data set to catalog by @millawell in https://github.com/HTR-United/htr-united/pull/51 Create biblia.yml by @alix-tz in https://github.com/HTR-United/htr-united/pull/52 New Contributors @PonteIneptique made their first contribution in https://github.com/HTR-United/htr-united/pull/6 @alix-tz made their first contribution in https://github.com/HTR-United/htr-united/pull/11 @FloChiff made their first contribution in https://github.com/HTR-United/htr-united/pull/15 @CVidalG made their first contribution in https://github.com/HTR-United/htr-united/pull/44 @millawell made their first contribution in https://github.com/HTR-United/htr-united/pull/51 Full Changelog: https://github.com/HTR-United/htr-united/commits/v0.1.0
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.723 | 0.831 |
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".