Science Metadata Management, Interoperability and Data Citations of the National Institute of Polar Research, Japan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Polar Data Centre (PDC) of the National Institute of Polar Research (NIPR) has a responsibility to manage polar science data as part of the National Antarctic Data Centre and the Science Committee on Antarctic Research. During the International Polar Year (IPY 2007–2008), a remarkable number of data/metadata involving multi-disciplinary science activities were compiled. Although the long-term stewardship of the accumulation of metadata falls to the data center of NIPR, the work has been in collaboration with the Global Change Master Directory, the Polar Information Commons, the World Data System and other data science bodies/communities under the International Council for Science. In addition, links with other data centers, such as the Data Integration and Analysis System Program of the Global Earth Observation System of Systems and the Polar Data Catalogue of Canada were initiated in 2014 using the Open Archives Initiative Protocol for Metadata Harvesting. The metadata compiled by the PDC were recently modified using an automatic attributing system and DataCite through the Japan Link Center.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.196 |
| Open science | 0.050 | 0.055 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it