Bibliographic record
Abstract
AUSTRALIA – Chronic pain research delves into the brain KOREA – STC life, Ltd. successfully treats stroke patients at Stem Cell Research Treatment Center MALAYSIA – Indigenous people ‘at graver risk’ of neglected diseases SINGAPORE – A*STAR scientists create stem cells from a drop of blood THE PHILIPPINES – ‘Too many exotic species’ in Philippine greening plan AFRICA – The parasite that escaped out of Africa CANADA – Genome British Columbia researchers closing in on chlamydia vaccine EUROPE – Teesside University pioneering life-saving research EUROPE – Inactivated polio vaccines broadly available for the world's children in the drive toward polio eradication EUROPE – Vitamin D deficiency may compromise immune function EUROPE – Inflammation mobilizes tumor cells NEPAL – Animal-borne parasites plague Nepal UNITED STATES – Zebrafish discovery may shed light on human kidney function UNITED STATES – Nanoparticles and magnetic fields train immune cells to fight cancer in mice UNITED STATES – Building heart tissue that beats UNITED STATES – Immunology researchers uncover pathways that direct immune system to turn ‘on’ or ‘off’ UNITED STATES – How diabetes drugs may work against cancer UNITED STATES – Study reveals how a protein common in cancers jumps anti-tumor mechanisms
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.808 | 0.619 |
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".