Bibliographic record
Abstract
A boriginal Canadians bear a disproportionate risk ofinjury and illness compared with their non-Aboriginal counterparts.1 Age-standardized, all-cause mortality rates for our Aboriginal population are al-most twice those of the whole population of Canada, men (561 v. 340 per 100 000) and women (335 v. 172 per 100 000) alike.2 A major contributor to these differences is traumatic injury and death, accounting for one-third of all deaths in the Aboriginal population.2 The article by Karmali and colleagues3 in this issue (see page 1007) is an important first step toward understanding this problem. This population-based, observational study describes the epidemiologic characteristics of severe trauma among status Aboriginal Canadians (First Nations individ-uals officially registered with the Canadian federal govern-ment under the Indian Act) within the Calgary Health Region. They found that severe trauma occurred almost 4
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 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.039 | 0.024 |
| Insufficient payload (model declined to judge) | 0.077 | 0.031 |
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".