Comparative organochlorine accumulation in two ecologically similar shark species (<i>Carcharodon carcharias</i> and <i>Carcharhinus obscurus</i>) with divergent uptake based on different life history
Bibliographic record
Abstract
Trophic position and body mass are traits commonly used to predict organochlorine burdens. Sharks, however, have a variety of feeding and life history strategies and metabolize lipid uniquely. Because of this diversity, and the lipid-association of organochlorines, the dynamics of organochlorine accumulation in sharks may be predicted ineffectively by stable isotope-derived trophic position and body mass, as is typical for other taxa. The present study compared ontogenetic organochlorine profiles in the dusky shark (Carcharhinus obscurus) and white shark (Carcharodon carcharias), which differ in metabolic thermoregulation and trophic position throughout their ontogeny. Although greater organochlorine concentrations were observed in the larger bodied and higher trophic position white shark (e.g., p,p'-dichlorodiphenyldichloroethylene: 20.2 ± 2.7 ng/g vs 9.3 ± 2.2 ng/g in the dusky shark), slopes of growth-dilution corrected concentrations with age were equal to those of the dusky shark. Similar ontogenetic trophic position increases in both species, less frequent white shark seal predation than previously assumed, or inaccurate species-specific growth parameters are possible explanations. Inshore habitat use (indicated by δ(13)C values) and mass were important predictors in white and dusky sharks, respectively, of both overall compound profiles and select organochlorine concentrations. The present study clarified understanding of trophic position and body mass as reliable predictors of interspecific organochlorine accumulation in sharks, whereas regional endothermy and diet shifting were shown to have less impact on overall rates of accumulation.
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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".