Use of the Zebra Mussel (Dreissena polymorpha) as a Bioindicator for Aromatic Hydrocarbons in Hamilton Harbour
Bibliographic record
Abstract
Abstract Three methods for the extraction of polycyclic aromatic hydrocarbons (PAHs) from zebra mussels (Dreissena polymorpha) sampled from Hamilton Harbour were compared. Replicate freeze-dried mussel tissue samples were extracted using acid digestion, tissue homogenization (mechanical extraction) and ultra-sonication. Each extract was submitted to a cleanup procedure (alumina chro-matography and Sephadex LH20 gel chromatography), followed by analysis using gas chromatography-mass spectrometry (GC-MS). The three extraction methods were equally efficient, based on a statistical comparison of mean concentrations of individual PAHs. Mussel extracts, when subjected to bioassays with Salmonella typhimurium strain YG1029 (TA100-like) in the presence of an exogenous metabolic activation system (S9), exhibited significant mutagenic responses; these responses varied with the PAH content of the mussel extracts. Sources of PAHs in mussel extracts were determined by examining the profiles of sulfur-containing polycyclic aromatic compounds (thia-arenes). Comparison of the ratios of certain thia-arenes with ratios in source samples enabled identification of vehicular emissions and coal tar-contaminated sediment as two sources of PAH contamination in Hamilton Harbour.
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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 | 0.000 | 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) | 0.000 | 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".