Development of a Copper Database for Exposure-Response Analysis
Bibliographic record
Abstract
While its precise form has not yet been defined, it is assumed that the exposure-response curve for copper (Cu) is U-shaped, as both Cu deficiency and excess can produce adverse health effects. Exposure-response relationships provide the foundation for setting recommended levels of exposure to essential and nonessential substances. A group of toxicologists, biologists, and epidemiologists formed a working group in 2002 to review the literature on Cu excess and deficiency as well as possible exposure-response modeling strategies. It was decided that in order to conduct more complex modeling exercises with studies on Cu, the information had to be organized into a database for application of emerging analytical approaches in exposure-response assessment. The database would support both current as well as proposed methods for exposure-response assessment and accommodate a variety of reporting methods found in the literature. As there are multiple studies looking at a wide range of adverse health effects attributed to excess and deficiency, data were organized into ordered categories of severity to create a common measure of response. The present study (1) outlines the approach used to identify studies for the Cu database based on their quality and usefulness for exposure-response analyses; (2) provides an overview of the process used to define a common dose metric; and (3) describes the process used to categorize a diverse number of responses from Cu excess and deficiency to an ordinal severity score. Efforts are underway to use this database to define the exposure-response curve for Cu excess and deficiency; however, the comprehensive database can be used to carry out other in-depth analyses on Cu toxicity.
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 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 | 0.058 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".