Mercury biomagnification in the food chain of a piscivorous turtle species (Testudines: Chelidae: Chelus fimbriata) in the Central Amazon, Brazil
Bibliographic record
Abstract
Abstract Turtles are an excellent biological model for studies of heavy metal contamination due to their natural history and ecological attributes. Turtles have a large geographical distribution, occupy different aquatic habitats, and pertain to various trophic levels. The present study investigated mercury bioaccumulation in the carnivorous chelonian Chelus fimbriata (Matamata turtle) and Hg biomagnification in relation to its aquatic food chain in the middle Rio Negro, Amazonas, Brazil. Tissue samples of muscle, carapace (shell) and claws were collected from 26 C. fimbriata, and autotrophic energy sources found in the turtle’s aquatic habitat area. In addition, samples of dorsal muscle tissue were collected from 7 Cichla. The samples were collected in February-March of 2014 and analyzed for THg concentrations and carbon (δ13C) and nitrogen (δ15N) stable isotopes. The highest THg concentrations were found in claws (3780ng.g-1), carapace (3622ng.g-1) and muscle (403ng.g-1), which were found to be significantly different (F(2.73)=49.02 p<0.01). The average δ13C and δ15N values in Matamata samples were 11.9‰ and -31.7‰, respectively. The principal energy source sustaining the food chain of C. fimbriata was found to be plankton and periphyton, while δ15N values showed its trophic position to be 3 levels above the autotrophic energy sources. There was a positive correlation between THg concentrations and turtle size, while a significant relationship was found between THg and δ15N, showing strong biomagnification in the food chain of C. fimbriata y=0.13x+0.97; r²=0.31). However, total mercury concentrations found in Matamata turtles were below the consumption threshold indicated by the WHO and Brazilian Health Ministry.
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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 | 0.000 | 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".