Bibliographic record
Abstract
To find the love of their life, some people enlist the help of a matchmaker. Caleb M. Brown, on the other hand, enlisted the help of a dinosaur. Last month, Brown and colleague Donald M. Henderson of Alberta’s Royal Tyrrell Museum of Palaeontology published a paper describing the discovery of a previously unknown relative of the triceratops (Curr. Biol. 2015, DOI: 10.1016/j.cub.2015.04.041). It was an exciting announcement made more so by what Brown slipped into the paper’s acknowledgments section: “C.M.B. would specifically like to highlight the ongoing and unwavering support of Lorna O’Brien. Lorna, will you marry me?” O’Brien, a paleobiologist at the Royal Tyrrell Museum, said yes—a satisfying conclusion to Brown’s research into regaliceratops, a prehistoric reptile whose name comes from the regal-looking crown of horns that decorates its head. Unearthed in southern Alberta, regaliceratops brings new insight to the evolutionary development of horned dinosaurs. Scientists have long categorized ...
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".