Besin zinciri biyobirikim modellemesi kullanarak insanların organik kimyasallara balık tüketimi yoluyla maruziyetinin tahmini
Bibliographic record
Abstract
In this study, the Aquatic Foodweb and TMF Model was used to predict bioaccumulation potentials of hydrophobic organic pollutants monitored in lakes in Türkiye. Data available in the literature was used for predicting specie chemical concentrations in pelagic, demersal and combined food webs. These data were then used to assess risks to human health via consumption of fish. Sensitivity analysis yielded logKow and chemical concentration in water as sensitive parameters for all species, whereas lipid content of detritus and phytoplankton had a great impact on the chemical concentration in organisms. Model was validated with data from Lake Ontario, Canada, and Lake Efteni and Lake Karaboğaz from Türkiye. Overprediction was observed, which was attributed to lack of: (i) site-specific information on food web and other environmental data, (ii) elimination mechanisms, (iii) incorporation of bioavailability of chemicals. The model was applied to determine organochlorine pesticide concentrations in tench and common carp from Lake Sapanca and sand smelt from Lake İznik. Human health risk assessment was conducted for both carcinogenic and non-carcinogenic health impacts on adults, adolescents and children. The outcomes reveal that the carcinogenic and non-carcinogenic risks associated with the consumption of fish from Lake Sapanca and Lake İznik are above acceptable limits, although overestimation potential is present. Chemical concentrations in these water resources are above the environmental quality standards, which result in potential health risks associated with fish consumption, as per model results. This thesis study underscores potential chemical related dangers originating from these lakes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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; both teacher heads agree on what is shown here.
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".