Mercury bioaccumulation in mussels in the Minas Basin: a comparison of soft tissues and shells as bioindicators
Bibliographic record
Abstract
This project investigated mercury contamination in coastal mussels at the Minas Basin, Bay of Fundy. This research aims to evaluate whether mussels are a valid and reliable biomarker of coastal mercury pollution. There was low contamination at the sampling sites (mean sediment total mercury = 5.1 ng/g dry weight (d.w.)). The mean concentration of total mercury in the mussel tissues was 62.3 ng/g d.w. (SD = 13.7 ng/g d.w.; n = 57). Through a regression analysis, we determined that total mercury and methylmercury in tissues were significantly negatively related with the mussel's condition index (p < 0.001, R = -0.5, R2 = 0.24 in both cases). Additionally, we found a negative and significant linear relationship between the logarithm of the whole organism soft tissue mass (d.w.) and the logarithm of the total mercury content (p < 0.001; R2 = 0.23) Through a Pearson correlation, shell length was found to have a negative correlation with the total mercury in soft tissue samples (p value = 0.01, R = -0.3). The mean concentration of methylmercury in soft tissues was 13.2 ng/g d.w. (SD = 6.3 ng/g d.w.), equivalent to 1.7 ng/g wet weight (w.w.). This is lower than Environment Canada's tissue residue guideline for effects on aquatic organisms for methylmercury (33 ng/g w.w.) In the mussel shells, total mercury in all samples was below the method detection limit (MDL = 1.7 ng/g d.w.). As such, for this study, the shells could not be used as a bioindicator of soft tissue concentration of mercury.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".