Dose-Response Modelling and Optimization of Quantitative High-Throughput Screening Assays
Bibliographic record
Abstract
Human health risk assessment is a process designed to characterize potential health risks associated with exposure to environmental agents.Classically, it involves four main steps:(1) hazard identification, (2) dose-response assessment, (3) exposure assessment, and (4) risk characterization.Traditionally, toxicological testing has relied heavily on experimental animals to predict potential human health risk.Motivated in part by the 2007 U.S. National Research Council report, Toxicity Testing in the 21 st Century: A Vision and aStrategy, there has been a shift towards new approach methodologies using in vitro, in silico, and in chemico techniques.Quantitative high-throughput screening (qHTS) is one such methodology that can rapidly produce in vitro assays for thousands of chemicals that can be analyzed for hazard identification and dose-response assessment purposes.I owe my greatest appreciation to my family for their support and love they provided me throughout my entire life.I would like to further thank my girlfriend, Hui Zhang, for providing daily support and warm encouragement to help me achieve this milestone.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".