An analysis of the link between strokes and soils in the South Carolina coastal plains
Bibliographic record
Abstract
The Stroke Belt is a geographical region of the Southeastern United States where resident individuals suffer a disproportionately higher rate of strokes than the rest of the population. While the "buckle" of this Stroke Belt coincides with the Southeastern Coastal Plain region of North and South Carolina and Georgia, there is a paucity of information pinpointing specific causes for this phenomenon. A number of studies posit that an exposure event-potentially microbial in nature-early in life, could be a risk factor. The most likely vector for such an exposure event would be the soils of the Southeastern Coastal Plain region. These soils may have chemical and physical properties which are conducive to the growth and survival of microorganisms which may predispose individuals to stroke. To this aim, we correlated SC stroke mortality data to soil characteristics found in the NRCS SSURGO database. In statewide comparisons, depth to water table (50 to 100 cm, R = 0.62) and soil drainage class (poorly drained, R = 0.59; well drained, R = -0.54) both showed statistically significant relationships with stroke rate. In a 20 county comparison, depth to water table, drainage class, hydric rating (hydric soils, R = 0.56), and pH (very strongly acid, R = 0.66) all showed statistically significant relationships with stroke rate. These data should help direct future research and epidemiology efforts to pinpoint the exact exposure events which predispose individuals to an increased stroke rate.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".