SEBI: An Architecture for Biomedical Image Discovery, Interoperability and Reusability Based on Semantic Enrichment.
Bibliographic record
Abstract
Images depicting key findings of research papers contain rich information derived from a wide range of biomedical experiments, e.g. charts, gels, anatomical features, and protein or DNA sequence alignments. Efficient practices for accessing biomedical images are key to allowing the timely transfer of information from the research community to peer investigators and other healthcare practitioners. Searching for images of a certain type is error prone as images are still opaque to information retrieval and knowledge extraction engines due to the absence of explicit descriptions or annotation of the image contents. Moreover, traditional biomedical search engines which search image captions for relevant keywords only offer syntactic search mechanisms without regard for the exact meaning of the query. In order to resolve these challenges and to support interoperability and reusability of biomedical images, we propose a general framework for semantic enrichment of biomedical images called SEBI. SEBI utilizes the information extracted from images as seed data to harvest new annotations from heterogeneous online biomedical resources. The framework incorporates a variety of knowledge infrastructure components and services including image feature extraction, Semantic Web data services, linked open data and the crowd-sourced annotation. Together, these resources make it possible to automatically and/or semi-automatically discover and semantically interlink new information in a manner that supports semantic search for images. Project Page: https://code.google.com/p/sebi/
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".