Finding Science with Science: Evaluating a Domain and Scientific Ontology User Interface for the Discovery of Scientific Resources
Bibliographic record
Abstract
Abstract Current approaches to the discovery of scientific resources (publications, data sets and web services) are dominated by keyword search. These approaches do not allow scientists to search on the deeper semantics of scientific resources, or to discover resources on the basis of the scientific approaches taken. This article evaluates a user interface that allows users to discover scientific resources through structured knowledge in the form of ontologies describing the domain and the scientific knowledge inherent within the scientific resource, and also through informal user tags. These combined capabilities provide scientists with new and powerful options for resource discovery. A qualitative user evaluation explored how scientists felt about the approach for resource discovery in the context of their scientific work. The study showed that marine scientists were enthusiastic about the capabilities of such an approach and appreciated the ability to browse the visual structure of the knowledge and query on scientific method but, overall, preferred the use of tags over ontologies. The exploratory nature of the user study was used to identify future directions for such improvements.
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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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".