How Discovery Systems Use DataCite Metadata: Harvester Roundtable
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- Science and technology studies, Scholarly communication
- Consensus categories
- Scholarly communication
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Not applicableConsensus signal: none
- Genre
- Candidate signal: MethodsConsensus signal: none
- Teacher disagreement score
- 0.989
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.901 | 0.927 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.152 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
In this session, we will provide an overview of some of the cutting edge tools and services available for working with DataCite metadata, including our APIs, DataCite Commons, the public data file, and beyond. Alongside this, we will hear from some of the key players who are leveraging DataCite’s 50+ million metadata records to develop innovative tools for locating research. This will be an opportunity to learn about how DataCite metadata is and can be used, to enable discovery and reuse, and the impact that rich metadata can have on the scholarly record. Speakers, chapters of the recording: Kelly Stathis (Technical Community Manager, DataCite), https://www.youtube.com/watch?v=dJgohsagG20&t=0s Maria Gould (Director of Product, DataCite), https://www.youtube.com/watch?v=dJgohsagG20&t=97s Paolo Manghi (Chief Technology Officer, OpenAIRE AMKE), https://www.youtube.com/watch?v=dJgohsagG20&t=1157s Patricia Tortosa (Editorial Content Manager, Clarivate Analytics), https://www.youtube.com/watch?v=dJgohsagG20&t=2030s Casey Meyer (Chief Technology Officer, OurResearch), https://www.youtube.com/watch?v=dJgohsagG20&t=2918s
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.
The record
- Venue
- Zenodo (CERN European Organization for Nuclear Research)
- Topic
- Research Data Management Practices
- Field
- Computer Science
- Canadian institutions
- OpenAlex
- Funders
- not available
- Keywords
- MetadataKey (lock)Public accessInformation technology
- Has abstract in OpenAlex
- yes