Special issue on Linked Data for Health Care and the Life Sciences
Bibliographic record
Abstract
Health Care and Life Sciences (HCLS) have long been a test-bed for the standards proposed by the W3C to build the Semantic Web1: since HCLS is descriptive by nature and its descriptions have traditionally been produced according to ad-hoc schemas in isolated resources, HCLS offers an ideal use case for technologies like RDF2, SPARQL3 and OWL4 [1,4]. This “marriage” of the HCLS domain with semantic technologies has resulted in a collection of resources that can be regarded as an HCLS-focused working implementation of the idea of the Semantic Web: the socalled Life Sciences Semantic Web (LSSW). As part of the process of implementing the LSSW, the HCLS community has adopted the Linked Data practices to publish information in a machine-friendly and linkable fashion [3], as a “down-to-earth” version of a prospective fully-fledged Semantic Web. This has resulted in members of the HCLS community, like the W3C HCLS Interest Group5, considerably contributing to the Linked Open Data (LOD) endeavour, with datasets like Bio2RDF [2] and Linked Open Drug Data (LODD) [5]. As the LOD network grows, producers and consumers alike are facing new challenges regarding interoperable vocabularies, filtering, graphical interfaces,
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.128 | 0.050 |
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".