ToxChip PCR Arrays for Two Arctic-Breeding Seabirds: Applications for Regional Environmental Assessments
Bibliographic record
Abstract
Increasing pollution in the Arctic poses challenges in terms of geographical and ecological monitoring. The Baffin Bay-Davis Strait (BBDS) region in the Canadian Arctic Archipelago is of particular concern due to the potential for increased shipping traffic and oil exploration. However, data on background contaminants associated with oil exploration/spills/natural seeps (e.g., polycyclic aromatic compounds [PAC]) and measures of potential effects for Arctic birds are limited. We developed a toxicogenomics approach to investigate the background gene expression profiles for two Arctic-breeding seabirds, the thick-billed murre ( Uria lomvia ) and the black guillemot ( Cepphus grylle ), which will aid effects-based monitoring efforts. Chemical burdens (53 PACs and 5 trace elements) and transcriptomic profiles (31 genes using a ToxChip PCR array) were examined in liver tissues ( n = 30) of each species collected from the Qaqulluit and Akpait National Wildlife Areas in the BBDS region. While chemical and transcriptomic profiles demonstrated low variability across individuals for each species, gene expression signatures were able to distinguish guillemots collected from two distinct colonies. This toxicogenomics approach provides benchmark data for two Arctic seabirds and is promising for future monitoring efforts and strategic environmental assessments in this sensitive ecosystem and areas elsewhere in the circumpolar Arctic that are undergoing change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".