Estimation of Uncertainty in Air–Water Exchange Flux and Gross Volatilization Loss of PCBs: A Case Study Based on Passive Sampling in the Lower Great Lakes
Bibliographic record
Abstract
Compared with dry and wet deposition fluxes, air–water exchange flux cannot be directly measured experimentally. Its model-based calculation contains considerable uncertainty because of the uncertainties in input parameters. To capture the inherent variability of air–water exchange flux of PCBs across the lower Great Lakes and to calculate their annual gross volatilization loss, 57 pairs of air and water samples from 19 sites across Lakes Erie and Ontario were collected using passive sampling technology during 2011–2012. Error propagation analysis and Monte Carlo simulation were applied to estimate uncertainty in the air–water exchange fluxes. Results from both methods were similar, but error propagation analysis estimated a smaller uncertainty than Monte Carlo simulation in cases of net deposition. Maximum likelihood estimations (MLE) of wind speed and air temperature were recommended to quantify the site-specific air–water exchange flux. An assumed 30–40% of relative uncertainty in overall air–water mass transfer velocity was confirmed. MLEs of volatilization fluxes of total PCBs across Lakes Erie and Ontario were 0.78 and 0.53 ng m –2 day –1, respectively, and gross volatilization losses of total PCBs over the whole lakes were 74 kg year –1 for Lake Erie and 63 kg year –1 for Lake Ontario. Mass balance analysis across Lake Ontario indicated that volatilization was the uppermost loss process of aqueous PCBs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".