Bibliographic record
Abstract
Now that the health and medical sector is slowly but surely beginning to embrace Web 2.0 technologies and tactics such as social networking, blogging, and sharing health information, such usage may become an everyday occurrence. This new trend is emerging under the Health 2.0 umbrella where it has important effects on the future of medicine. This chapter introduces some important Health 2.0 concepts and discusses their advantages for health care and medical practice. In addition, this chapter provides a case study for building a Semantic Blog for Gene Annotation and Searching (GAS) among social network users. The GAS Blog enables users to syndicate and aggregate gene case studies via the RSS protocol, annotate gene case studies with the ability to add new tags (folksonomy), and search for/navigate gene case studies among a group or cross-groups based on FOAF, GO, and SCORM metadata. The GAS Blog is built upon an open source toolkit (WordPress) and further programmed via PHP. The GAS Blog is found to be very effective for annotation and navigation when compared with the traditional gene annotation and navigation systems, as well as with traditional search engines such as XPath.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".