Measurement and Modeling of Polychlorinated Biphenyl Bioaccumulation from Sediment for the Marine Polychaete <i>Neanthes arenaceodentata</i> and Response to Sorbent Amendment
Bibliographic record
Abstract
Bioaccumulation rates of polychlorinated biphenyls (PCBs) for the marine polychaete Neanthes arenaceodentata were characterized, including PCB uptake rates from water and sediment, and the effect of sorbent amendment to the sediment on PCB bioavailability, organism growth, and lipid content. Physiological parameters were incorporated into a biodynamic model to predict contaminant uptake. The results indicate rapid PCB uptake from contaminated sediment and significant organism growth dilution during time-series exposure studies. PCB uptake from the aqueous phase accounted for less than 3% of the total uptake for this deposit-feeder. Proportional increase of gut residence time and assimilation efficiency as a consequence of the organism's growth was assessed by PCB uptake and a reactor theory model of gut architecture. Pulse-chase feeding and multilabeled stable isotope tracing techniques proved high sediment ingestion rates (i.e., 6-10 times of dry body weight per day) indicating that such deposit-feeders are promising biological indicators for sediment risk assessment. Activated carbon amendment reduced PCB uptake by 95% in laboratory experiments with no observed adverse growth effects on the marine polychaete. Biodynamic modeling explained the observed PCB body burdens for N. arenaceodentata, with and without sorbent amendment.
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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".