Evaluation of persistent hydrophobic organic compounds in the Columbia River Basin using semipermeable‐membrane devices
Bibliographic record
Abstract
Abstract Persistent hydrophobic organic compounds are of concern in the Columbia River because they have been correlated with adverse effects on wildlife. We analysed samples from nine main‐stem and six tributary sites throughout the Columbia River Basin (Washington and Oregon) for polychlorinated dibenzo‐p‐dioxins, dibenzofurans, polychlorinated biphenyls, organochlorine pesticides, and priority‐pollutant polycyclic aromatic hydrocarbons. Because these compounds may have important biological consequences at aqueous concentrations well below the detection limits associated with conventional sampling methods, we used semipermeable‐membrane devices to sample water and achieved parts‐per‐quintillion detection limits. All of these compound classes were prevalent within the basin, but concentrations of many analytes were highest in the vicinity of Portland–Vancouver, indicating that the Willamette subbasin—and perhaps the urban area in particular—is an important source of these compounds. Data collected during basin low‐flow conditions in 1997 and again during basin high‐flow conditions in 1998 indicate that in‐stream processes such as dilution by relatively clean inflows, and flow through island hyporheic zones may be important mechanisms for attenuating dissolved concentrations of hydrophobic compounds. Published in 2001 by John Wiley & Sons, Ltd.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".