Report Summary - Perinatal Health Indicators 2013: a Surveillance Report by the Public Health Agency of Canada’s Perinatal Surveillance System
Bibliographic record
Abstract
The maternal mortality rate is the number of maternal deaths (occurring during pregnancy, childbirth, or within 42 days of delivery or termination of pregnancy) divided by the number of deliveries.N The fetal mortality rate is the number of late fetal deaths per 1000 total births (live births and stillbirths).N The infant mortality rate is the number of deaths of live-born babies in the first year after birth per 1000 live births.N Neonatal death is the death of a newborn aged 0-27 days.N Post-neonatal death is the death of an infant aged 28-364 days. NThe preterm birth rate is the number of live births with a gestational age at birth of less than 37 completed weeks as a proportion of all live births. NThe postterm birth rate is the number of live births with a gestational age at birth of 42 or more completed weeks of pregnancy as a proportion of all live births.N The small-for-gestational-age birth rate is the number of singleton live births whose birth weight is below the 10 th percentile of the sex-specific birth weight for gestational age reference as a proportion of all singleton live births. NThe large-for-gestational-age birth rate is the number of singleton live births whose birth weight is above the 90 th percentile of the sex-specific birth weight for gestational age reference as a proportion of all singleton live births.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".