ICAT: Integrating Data Infrastructure for Facilities Based Science
Bibliographic record
Abstract
Scientific facilities, in particular large-scale photon and neutron sources, have demanding requirements to manage the increasing quantities of experimental data they generate in a systematic and secure way. In this paper, we describe the ICAT infrastructure for cataloguing facility-generated experimental data which has been in development within STFC and DLS for several years. We consider the factors which have influenced its design and describe its architecture and metadata model, a key tool in the management of data. We go on to give an outline of its current implementation and use, with plans for its future development.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".