Bioaccumulation and Trophic Magnification of Short- and Medium-Chain Chlorinated Paraffins in Food Webs from Lake Ontario and Lake Michigan
Bibliographic record
Abstract
Chlorinated paraffins (CPs) are complex mixtures of chlorinated alkanes used in a myriad of industrial applications as flame retardant plasticizers and additives. In this study, the distribution and bioaccumulation/biomagnification of short-chain CPs (C10-C13, SCCPs) and medium-chain CPs (C14-C17, MCCPs) were investigated in samples collected between 1999 and 2004 from Lake Ontario and northern Lake Michigan. Total (sigma) SCCPs and sigmaMCCPs concentrations in water from Lake Ontario were 1190 pg/L and 0.9 pg/L (data from 2004 only), respectively. CPs were also detected in invertebrates and fish from both lakes. SCCP predominated in organisms from Lake Michigan with the highest mean concentrations found in lake trout [Salvelinus namaycush, 123 +/- 35 ng/g wet weight (ww)]. In Lake Ontario, MCCPs predominated in most species with the highest levels detected in slimy sculpin (Cottus cognatus, 108 ng/g ww) and rainbow smelt (Osmerus mordax, 109 ng/g ww). Bioaccumulation and biomagnification of CPs was evaluated on an isomer basis (i.e., C10H17Cl5, C10H16Cl6, etc). Log bioaccumulation factors for lake trout (lipid based) ranged from 4.1 to 7.0 for SCCPs and 6.3 to 6.8 for MCCPs. SCCPs and MCCPs were found to biomagnify between prey and predators from both lakes with highest values observed for Diporeia-sculpin (Lake Ontario, C15Cl9 = 43; Lake Michigan, C10Cl5 = 26). Trophic magnification factors for the invertebrates-forage fish-lake trout food webs ranged from 0.41 to 2.4 for SCCPs and from 0.06 to 0.36 for MCCPs. Given the prominence of CPs, particularly in lake waters and in lower food web organisms, further investigation is needed to evaluate the magnitude of their distribution and accumulation/magnification in the Great Lakes environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".