Bibliographic record
Abstract
One of the major goals of the Great Lakes Action Plan is to actively accumulate and assess toxicological information on persistent toxic substances found in the Great Lakes basin. As part of Health Canada's commitment to this plan, a review of biomarkers for the environmental contaminants polychlorinated biphenyls (PCBs) and polychlorinated dibenzodioxins/dibenzofurans (PCDDs/PCDFs) was conducted. In general, while food consumption was identified as the major source of human exposure to both contaminant groups, certain commodities, such as fish, milk and dairy products, and meat, were found to predominate. Due to the ubiquitous nature of these environmental contaminants and their propensity to bioaccumulate, all humans will have detectable body burdens, which in certain cases can be positively associated with the consumption of particular foods (i.e., PCBs and freshwater fish from the Great Lakes). When dealing with environmental exposure only, relating specific effect biomarkers to contaminant exposure or tissue levels was difficult, due in part to the complex nature of the exposure and the nonspecific nature of the effect. For PCBs, the most likely biomarkers of effect included some form of alteration in lipid metabolism (serum triglyceride / cholesterol levels) and elevation of hepatic-related enzymes, aspartate aminotransferase (AST) and y-glutamyltransferase (GGT). Cross-species
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.065 | 0.024 |
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".