The Use of Categorical Regression in Modeling Copper Exposure-Response Relationships
Bibliographic record
Abstract
Characterization of the exposure-response relationship for copper (Cu) is an essential step in identifying a range of exposures that can prevent against toxicity from either excess or deficiency. Categorical regression is a exposure-response modeling technique that can be used to model data from multiple studies with diverse endpoints simultaneously by organizing the toxicity data into ordered categories of severity. This study describes how categorical regression can be used to model the exposure-response relationship for Cu and presents a preliminary analysis of the comprehensive database on Cu-induced toxicity due to either excess or deficiency. Categorical regression provides a useful tool for summarizing and describing the available data on Cu excess and deficiency, as well as in identifying data gaps in Cu exposure-response. This methodology also allows for a diverse database with considerable variability in animal species, strain, age, and study design to be analyzed in its entirety. The present application of the Cu toxicity database suggests that there is a lack of information on the potential adverse health effects from chronic exposure to Cu; there are also a limited number of studies using marginally excess and deficient levels of Cu. The database presently includes insufficient data to create a complex model that accounts for a large proportion of the heterogeneity in toxicity seen among the available studies on Cu-induced toxicity. The current Cu database is presently being updated in order to permit more comprehensive categorical regression analyses with finer stratification options. The resulting exposure-response model could be used to provide information in the determination of an acceptable range of oral intake for Cu.
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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".