Towards a common data space - Promising standards for search, discoverability and interoperability
Bibliographic record
Abstract
Deutsch: Im Rahmen der NFDI4Biodiversity All Hands Conference vom 12.-14. Oktober 2022 stellt sich die Task Area 2 Measure 3 kurz vor und bietet einen Überblick über die Aktivitäten im Zuge der Harmonisierungsbemühungen im Bereich Metadaten und Datenaustausch des Konsortiums. Die beiden als vielversprechend identifizierten Kandidaten für die erleichterte Auffindbarkeit sowie der semantische Annotation von Daten der Biodiversitätsforschung im Allgemeinen ((bio)schema.org), und der detaillierten inhaltlichen Beschreibung von Daten biologischer Sammlungen im Speziellen werden kurz vorgestellt, um im Fall von bioschemas mit einigen Hintergründen unterfüttert. English: As part of the NFDI4Biodiversity All Hands Conference, October 12-14, 2022, Task Area 2 Measure 3 briefly introduces itself and provides an overview of activities underway in the Consortium's metadata and data exchange harmonization efforts. The two candidates identified as promising for facilitating discoverability as well as semantic annotation of biodiversity research data in general ((bio)schema.org), and detailed content description of biological collections data in particular are briefly presented, and are backed up with some background in terms of bioschemas mark-up.
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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 | 0.094 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.038 | 0.064 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".