First Assessment of Triazoles and Other Organic Contaminants in Snow and Snowmelt in Urban Waters, Anchorage, Alaska
Bibliographic record
Abstract
Snow scavenges organic contaminants from the atmosphere and has been shown to retain a variety of contaminants. Concentrated release of organic contaminants during onset of melting due to interstitial melting suggests an immediate transfer of organic contaminants from snow pack to surface water via storm drainages. Urban environments in a climate with extended periods of snow, such as Alaska, Maine, Minnesota, Canada, and Northern Europe, therefore may experience organic contamination of surface water during snowmelt. This study presents first results on a multidimensional assessment of concentration and changes of organic contaminants, specifically 1H-benzotriazole (BT) and its methylated forms, 4- and 5-methyl 1H-benzotriazole (tolyltriazole TT), during snowmelt in an urban creek that passes through Anchorage, Alaska and discharges into Cook Inlet and in two snow dump sites (snow and melt water). Results indicate concentrations up to 310 ng L-1 BT and 4490 ng L-1 TT during snowmelt. The timing of peak agrees with increasing levels of Na+ and Cl- and anthropogenic derived organic material based on fluorescence spectrometry. The concentrations of BT and TT are comparable to those in streams passing through larger industrial cities in Europe, and TT peak concentrations are even higher than those previously determined in streams in Europe and the U.S. In June, after spring melt, while overall DOC increases, concentrations of BT and TT return to levels close to detection limits (e.g. background concentrations) and fluorescence spectrometry indicate that organic carbon becomes more terrestrially derived.
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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 | 0.002 | 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".