Bibliographic record
Abstract
CINECA aims to support federated queries and analyses of distributed cohorts across continents. But human health datasets are extremely diverse; many different types of data are collected for many different kinds of health studies by many different health research communities. As a result, different cohort datasets often use different ontologies to describe similar kinds of entities, or represent concepts, such as genomic variation differently. CINECA must span this diversity of data representations in order to achieve its goals of connecting health research cohort data. The work of WP3 partially addresses discoverability of datasets by defining a standard minimal cohort-level data representation which will be common across all cohorts; but that does not address cohort-level data that falls outside of the minimal common data model, nor does it address the representation of patient-level data. WP1’s role is to design and deploy API access to both cohort- and patient-level data, and a fundamental functionality of the infrastructure is to allow the user to find the appropriate dataset independently of the ontology used to map locally the different cohorts or indifferently of the format and syntax used to describe the variants. This report describes the work done on query expansion, by implementing and demonstrating a query expansion service API that improves findability and searchability of distributed cohort data. Multiple kinds of query expansions are available for enabling further data integration and interoperability, including horizontal expansion, i.e., across ontological systems, and vertical expansion, i.e., within sublevels of the same ontological resource.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.052 |
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".