Updated problem formulation and protocol for the inorganic arsenic (iAs) IRIS assessment
Bibliographic record
Abstract
In May 2019, the U.S. Environmental Protection Agency (EPA) released the updated problem formulation and protocol for the iAs assessment. The updated problem formulation includes the refined scope that specifies which health outcomes are prioritized for dose-response analyses and toxicity value derivation. The protocol includes the methods and approaches proposed for use in developing the assessment. Epidemiology studies will be the focus of the assessment, consistent with prior National Research Council (NRC) of the National Academy of Science (NAS) input. EPA considered strength of the epidemiological evidence for hazard by relying on conclusions from assessments conducted by other health agencies or by conducting new systematic reviews of the existing literature. Based on qualitative hazard analyses of the iAs literature, the following health outcomes were identified for potential dose-response analyses based on sufficient human evidence: cancers of the bladder, lung, kidney, liver, and skin; and noncancer effects of iAs on the circulatory system (ischemic heart disease, hypertension, and stroke), reproductive system (including pregnancy and birth outcomes), developmental outcomes (including neurodevelopmental toxicity), endocrine system (including diabetes), immune system, respiratory system, and skin. The NRC released their review of these materials, supporting EPA&s;s refined scope and methods, in October 2019.
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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 | 0.083 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.117 | 0.025 |
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".