IMIA LaMB WG event: 'Biomedical Semantics in the Big Data Era', Workshop at MEDINFO 2015 – São Paulo,Brazil
Bibliographic record
Abstract
The workshop of IMIA WG 'Language and Meaning in Biomedicine' was held at MEDINFO 2015 – São Paulo,Brazil. This document set document has (1) all the presentation materials:<br> - Introduction, by Ronald Cornet<br> - From free text to ontology, by Stephane Meystre<br> - Bridging natural and formal languages for representing knowledge and information, by Stefan Schulz<br> - Deep question-answering for biomedical decision support, by Patrick Ruch<br> - Feature extraction for predictive modeling, by Jianying Hu<br> - Connecting structured and unstructured content , by Tomasz Adamusiak<br> - Summary and workshop organization, by Laszlo Balkanyi (2) a reader with a library of references for the MEDINFO 2015 workshop of IMIA LaMB Working Group A related publication (2015 Biom Sem Big Data Workshop Paper) is also available under the 'IMIA LaMB publications' chapter of this repository.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".