Bibliographic record
Abstract
INDIA – Climate change threatens India’s native plants. INDIA – Air pollution hits crops more than climate change. LAOS – Laos targets green energy in new Asian economic bloc. THE PHILIPPINES – Centuries-old traditional medicines still used in Palau. SINGAPORE – MerLion’s finafloxacin shows positive phase 2 results in complicated urinary tract infections. AFRICA – Biosciences research ‘key but gets small local support’. AFRICA – Biofortified maize ‘could control vitamin A deficiency’. AFRICA – New partnership for rice development in Africa formed. AFRICA – Ebola vaccine arrives in Liberia for large-scale trial. UNITED STATES – Researchers uncover key cancer-promoting gene. UNITED STATES – Brain scientists figure out how a protein crucial to learning and memory works. UNITED STATES – Scientists develop pioneering method to define stages of stem cell reprogramming. UNITED STATES – Researchers grow functional tissue-engineered intestine from human cells. UNITED STATES – A world first at the Montreal Heart Institute: Discovery of a personalized therapy for cardiovascular disease.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.760 | 0.595 |
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".