Quantitative biomonitoring of polychlorinated biphenyls in the Detroit River using the freshwater mussel Elliptio complanata.
Bibliographic record
Abstract
Accurate water concentration estimates are a modeling parameter that has been elusive. The need for time integrated estimates of the bioavailable fractions of persistent bioaccumulative and toxic substances has been difficult to approximate due to the lack of reliable sampling techniques. This thesis examined the utilization of mussels as quantitative biomonitors of polychlorinated biphenyl concentrations in water. A species of freshwater mussel, Elliptio complanata was calibrated to determine polychlorinated biphenyl elimination rates under both laboratory and field conditions. Elimination rate constants for non-Aroclor PCBs were investigated and compared with a previously published relationship for Aroclor PCBs in the same species. The investigation found that mussels allowed to depurate under an array of environmental conditions and deployment times exhibited similar elimination kinetics. ( k2 = -0.59(+/-0.05)·LogKow + 2.05(+/-0.28) (O'Rourke et al. 2004); k2 = -0.44(+/-0.10)·LogKow +1.3(+/-0.67) (Laboratory depuration study)). Sensitivity analysis indicated that the expected error associated with the steady state correction term would be less than 3 and 2 fold for PCBs with log Kow >7 when biomonitors are deployed for periods of 60 and 90 d, respectively. This indicates that errors associated with toxicokinetic parameters as determined under laboratory conditions are small and should contribute little error in the extrapolation of chemical residues measured in the biomonitor to ambient environmental concentrations. (Abstract shortened by UMI.) Source: Masters Abstracts International, Volume: 43-03, page: 0808. Adviser: Ken Drouillard. Thesis (M.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.000 |
| 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".