Bibliographic record
Abstract
The total number of 2020 resident births in South Dakota continues to decline with a 4 percent decrease from the previous year yielding the state's lowest crude birth rate (12.3 per 1,000 population) since its first recording in 1910. Currently, similar to the U.S., approximately one-quarter of all births are minority. The percentage of American Indian births is decreasing in its contribution to this population of the state with a growing percent of African American and multi-race newborns comprising the minority population in the state. South Dakota had one more infant death in 2020 (n=81) compared to 2019. The decrease in births led to a non-significant increase in the state's infant mortality rate (IMR) from 7.0 to 7.4 that is significantly higher than the U.S. rate (5.6) in 2019. An increase in nine sudden unexpected infant deaths (SUID) from 2019 to 2020 contributed to the rising IMR. Compared to the U.S., South Dakota has a lower percent of its infant deaths among those who are low birth weight (55 vs. 66 percent). Approximately one-third of white infant deaths occurred after the first 27 days of life; this was true for approximately half of all minority infants. Overall, South Dakota's minority infants have significantly higher rates of neonatal and post neonatal death than its whites, specifically due to perinatal causes, SUID, and accidents/homicide. How SUID contributes to the state's IMR is an area for needed attention as these deaths are increasingly known to accompany risks that, if alleviated, could prevent loss of early life. An examination of data from the year 2020 is the first opportunity to see possible relationships between perinatal outcomes and the pandemic that spanned approximately three-quarters of this year. Drawing causal relationships is not possible, but several observations about the impact of the pandemic are made as natality and infant mortality data for this year are explored in this annual report.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".