Seasonality in Bioaccumulation of Organochlorines in Lower Trophic Level Arctic Marine Biota
Bibliographic record
Abstract
Organochlorine (OC) pesticides in ice algae, phytoplankton, and microzooplankton during summer months and in meso- and macrozooplankton throughout 1993 in the Canadian archipelago (Barrow Strait) were compared with seasonal changes in seawater (upper 50 m) concentrations. α-HCH, HCB, ΣCHL, dieldrin, γ-HCH, ΣPCB, and ΣDDT (<100 ng g - 1 lipid, <10 ng g - 1 wet weight) were quantified. Meso- and macrozooplankton had higher levels of toxaphene (CHBs) (>100 ng g - 1 lipid, >10 ng g - 1 wet weight) than ice algae and phytoplankton. Highest OC concentrations occurred in macrozooplankton during the winter−spring period of ice cover. Concentrations for all compounds except HCHs decreased during the open water period when bioaccumulation factors (BAFs) (tissue:water concentrations) were maximum (10 6 −10 7 lipid weight basis) for CHBs and ΣDDT and minimum (10 3 −10 4 ) for HCHs. BAFs on a wet weight basis mirrored lipid-based values but were approximately 10-fold lower. Meso- and macrozooplankton had minimal BAFs in July and August when lipid levels were low (<3% wet weight) and suspended particulate matter concentrations were at their seasonal maximum. Slopes for regressions of log octanol−water partition coefficients for nine OCs and log BAF decreased from >0.9 during ice cover to <0.6 in the open water period. Deviations from physical-chemical equilibrium could reflect more continuous input, zooplankton growth with nonequilibrium partitioning, selective metabolism, and higher suspended particulate matter concentrations during the open water period.
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 | 0.000 |
| 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".