The International Workshop on Semantics-enabled Biomedical Literature Analytics (SeBiLAn)
Bibliographic record
Abstract
extended-abstract Free Access Share on The International Workshop on Semantics-enabled Biomedical Literature Analytics (SeBiLAn) Authors: Faezeh Ensan Ryerson University, Canada Ryerson University, CanadaSearch about this author , Halil Kilicoglu University of Illinois Urbana-Champaign, USA University of Illinois Urbana-Champaign, USASearch about this author , Bridget Mcinnes Virginia Commonwealth University, USA Virginia Commonwealth University, USASearch about this author , Lucy Lu Wang Allen Institute for AI, USA Allen Institute for AI, USASearch about this author Authors Info & Claims WWW '22 Companion: Companion Proceedings of the Web Conference 2022April 2022 Pages 818–820https://doi.org/10.1145/3487553.3524875Online:16 August 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.060 | 0.041 |
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".