Edible Seaweeds of the Salish Sea: Contaminant Levels and Comparison with Common Foods
Bibliographic record
Abstract
To increase our seafood safety knowledge with respect to seaweed, this study compares contaminant concentrations in three species of edible seaweeds (Fucus distichus, F. spiralis, and Nereocystis luetkeana) harvested from 43 locations within the Salish Sea from June to September 2015. Fucus spp. were analyzed for 162 chemicals: 17 metals, 94 persistent organic pollutants (POPs) and 51 polycyclic aromatic hydrocarbons (PAHs). Nereocystis luetkeana was analyzed for metal content. Two health-based screening levels were calculated, one on the U.S. Environmental Protection Agency (USEPA) Reference Dose (RfD) and the other on the USEPA Cancer Slope Factor (CSF) when these data were available. Concentrations of Cd, Pb, total PCBs and the PAH benzo(a)pyrene (BaP) at each site were compared to the screening levels (SLs). Concentration of Pb, Cd and Hg were also compared to the French regulations in 2014, for these metals in seaweeds. Generally, contaminants in Salish Sea seaweeds were below detection levels. Concentrations of total PCBs were all below the RfD SL but concentrations in F. distichus at ten of 43 sites and F. spiralis at one of three sites had concentrations above the cancer-based SL. Concentrations of all PAHs at all sites were below the RfD, but BaP concentrations of F. distichus at one of 43 sites and F. spiralis at one of three sites had concentrations above the cancer-based SL. Both sites were in Victoria Harbour, Canada. Screening levels could not be calculated for Pb because no RfDs and CSFs exist. Concentrations in F. distichus at three sites in Victoria Harbour were above the Frenchlegal limit (5 mg/kgdw) for edible seaweeds. Levels of Cd were lower than RfD-based SLs, however, all samples were higher than French legal limit (0.5 mg/kgdw). Total arsenic (tAs) was detected at all sites and ranged from (16-99 mg/kgdw). Concentrations of contaminants in serving sized portions of seaweed samples were compared to concentrations in portions of common foods and within the same general ranges for levels of PCBs, BaP, tAs, Pb, Hg, and Cd contaminants. An important note is that we have reanalyzed the data since the writing of this thesis, although, overall, the results and conclusions have not changed, I would direct you toward that publication for citations of my work (Hahn et al., 2021, in prep.).
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 | 0.000 | 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) | 0.001 | 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".