Bibliographic record
Abstract
Aquitaine Patrimoines is a cultural heritage portal or service provider in OAI terms. The portal attempts to pull together diverse cultural information sourced from libraries, media libraries, archives, museums, cultural heritage education centers, centers of documentation, etc. The data, harvested by means of the OAI protocol, describes various heritage resources concerning the Aquitaine region of France. The contributors and actors from international to local levels are interested in validating methodologies and technologies for sharing resources in a distributed environment as well as investigating the services which can be derived from these sources. The experiences encountered during the development of this portal brought to light the issues surrounding the creation an OAI service provider for cultural heritage purposes which ranged from technical to content concerns. http://ajlsm-sdx.hopto.org/sdx-22h/pa-portail/ Cyberdocs is a free/open-source platform for publishing structured electronic documents. Cyberdocs was realized as a result of experiences from the Cyberthees project, an information processing platform for scholarly publishing initiated by Presses de l'Universite de Montreal in 1997. The platform consists of modules serving three purposes: conversion, management, and publication (including OAI Repository and Harvester implementations). The use of standard technologies and open-source software to create this platform provides various technical and organizational benefits which can be of value to a larger community. The future of the Cyberdocs project will focus on incorporation more resources to support various types electronic structured documents, greater support for multilingualism in interfaces, and encouraging the involvement of more interested parties; therefore, allowing the platform to grow and become more useful to a larger public. http://sourcesup.cru.fr/cybertheses/ http://mirror-fr.cybertheses.org/ Both of these projects incorporate SDX, an open-source system for searching and publishing XML documents, which is built upon the Apache Cocoon framework and incorporates the Apache Lucene search-engine. http://sdx.culture.fr/sdx/ (documentation in English currently NOT available) http://cocoon.apache.org http://jakarta.apache.org/lucene/
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.026 |
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".