Concentrations, Trends, and Air–Water Exchange of PCBs and Organochlorine Pesticides Derived from Passive Samplers in Lake Superior in 2011
Bibliographic record
Abstract
The largest fresh water body in North America, Lake Superior, has the potential to both accumulate and serve as a secondary source of persistent bioaccumulative toxins, such as polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs). Polyethylene passive samplers (PEs) were thus simultaneously deployed at 19 sites in surface water and near surface atmosphere across Lake Superior to determine air and water concentrations and air–water gradients of 18 PCBs and 24 OCPs. PCBs in the air and water were characterized by penta- and hexachlorobiphenyls with distribution along the coast correlated with proximity to developed areas. Surface water and atmospheric concentrations were dominated by α-HCH (average 250 pg L –1 and 4.2 pg m –3, respectively), followed by HCB (average 17 pg L –1 and 89 pg m –3, respectively). Decreases in open lake concentrations of PCBs in water and air from spring to summer were consistent with ongoing volatilization from the surface layer as the main cause. Conversely, α-endosulfan was consistently deposited into the surface water of Lake Superior. Results indicated that PCBs were depleted in the surface mixed layer, implying a physical limit on evasive fluxes. This was corroborated with measurements from a vertical profile, displaying greater PCB concentrations at depth.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".