Additional file 1 of The gap in life expectancy and lifespan inequality between Iran and neighbour countries: the contributions of avoidable causes of death
Bibliographic record
Abstract
Additional file 1: Table A1. Age- and cause-specific contributions to gap in life expectancy between Iran and the neighbour countries in males. Table A2. Age- and cause-specific contributions to gap in life expectancy between Iran and the neighbour countries in females. Table A3. Age- and cause-specific contributions to gap in lifespan inequality between Iran and the neighbour countries in males. Table A4. Age- and cause-specific contributions to gap in lifespan inequality between Iran and the neighbour countries in females. Table A5. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in males based on the life table from the institute for health metrics and evaluation. Table A6. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in females based on the life table from the institute for health metrics and evaluation. Table A7. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in males based on the life table from the United Nations. Table A8. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in females based on the life table from the United Nations. Table A9. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in males based on the life table from the world health organization. Table A10. Age- and cause-specific contributions to gap in life expectancy and lifespan inequality between Iran and the neighbour countries in females based on the life table from the world health organization.
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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 | 0.002 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.780 | 0.096 |
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".