Prevalence and potential for biological effects of several chemicals of interest in Lake Superior tributaries
Bibliographic record
Abstract
Lake Superior is typically assumed to have excellent water quality, but its tributaries have been understudied for contaminants relative to other Great Lakes. This study monitored polycyclic aromatic hydrocarbons (PAHs), alkylphenols (AP) and alkylphenol ethoxylates (APEs), bisphenols, and neonicotinoid pesticides in 18 sites on 17 United States tributaries to Lake Superior in 2022. Contaminants were analyzed from 54 surface water and 17 stream bed sediment samples. PAHs were the most prevalent contaminant class. Among 17 PAHs in 17 samples (289 instances), 71 % were positive detections. Surface water samples were not analyzed for PAHs. Next, among 4 APs and APEs analyzed, 35 % of sediment sample instances and 21 % of instances in 54 water samples were positive detections. Bisphenol analogues were detected in 2 % of sediment sample instances and 4 % of water sample instances. Bisphenol A (BPA) was not quantified due to field blank contamination, but bisphenol AF (BPAF), bisphenol E (BPE), bisphenol F (BPF), and bisphenol S (BPS) were detected in environmental samples. Neonicotinoids were detected in 0.5 % of sediment sample instances and 0.4% of water samples instances. Low detection frequencies for contaminants precluded analyses with watershed land use characteristics, but four sites (Tischer Creek, Miller Creek, Carp River and Iron River) emerged as locations with relatively elevated contaminant concentrations. Comparison of concentrations with ToxCast and other water quality guidelines suggested potential for biological effects from some samples. This study provides a baseline of low contaminant prevalence in Lake Superior tributaries while identifying a few locations that may warrant further monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".