Bibliographic record
Abstract
On a snowy November day in 1964, a team of about 40 doctors and scientists boarded the Royal Canadian Navy’s H.M.C.S. Cape Scott in Halifax, Nova Scotia. They were headed to Easter Island, a triangle-shaped speck in the South Pacific that’s 2,200 km from its nearest inhabited neighbor. Their goal: to study a group of people—their heredity, environment, and common diseases—who lived in this uniquely remote spot before the Chilean government disrupted their isolation with an airstrip on the island’s southwestern corner. As the scientists set out, no one could have predicted that the Canadian expedition’s most valuable finding would come from a bit of bacterium ensconced in a sample of Easter Island’s soil. Georges Nógrády wasn’t looking for cures when he divided Easter Island into 67 parcels and took a soil sample from each. The University of Montreal microbiologist was trying to understand why the islanders weren’t afflicted with
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.114 | 0.090 |
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".