ISDP 2014 Abstracts
Bibliographic record
Abstract
In January 2014, an industrial storage tank containing multiple coal processing chemicals ruptured and spilled approximately 37,850 liters of Crude MCHM and polyglycol ethers into the Elk River in West Virginia.This environmental disaster polluted drinking water for more than 300,000 residents, many of whom were pregnant women, infants, and children.These substances cause eye/skin irritation and have been linked to kidney damage and hemoglobin anomalies in rats and humans.What is presently unknown is whether these substances affect physiological, neurological, and behavioral development in young organisms.We examined the effects of tripropylene glycol methyl ether (TPGME, one isomer of polyglycol ethers) and PPh 3 (a substance chemically similar to Crude MCHM) on early physiological development and later behavioral, motor, and social development in zebrafish (Danio rerio).Zebrafish provide an excellent model for toxicology studies, as they develop within transparent eggs that provide a clear "window" into embryological development.They are used extensively throughout the biological, biochemical, and developmental sciences to study neurological and behavioral systems, and have recently shown promise as practical screens for chemical toxicology studies.In this study, zebrafish embryos were exposed to 2%, 5%, or 10% solutions of either TPGME or PPh 3 during the embryonic stage (days post-fertilization, dpf: 1-5), a period analogous to the prenatal period in human development.Results showed that both of these substances significantly compromise developmental physiology in embryonic zebrafish when compared to controls.Preliminary tests also reveal that these substances appear to negatively affect developing motor, behavioral, and social systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.611 | 0.480 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".