Bioaccumulation of trace metals in biota (algae and chironomids) from Kenyan saline lakes (Bogoria and Nakuru): evaluation and verification of two compartment toxicokinetic models
Bibliographic record
Abstract
This study was carried out to assess the suitability of various aquatic biota particularly those associated with some Kenyan Saline lakes, as biomonitors of trace metals. The study also aimed at evaluating the use of two-compartment and logistic regression models as predictive tools in assessment of environmental quality in the specific ecosystems. Experimental organisms namely, algae (Arthrospira fusiformis) and chironomids (Lepotochironomous deribae) among others, were obtained particularly from, Lakes Bogoria and Nakuru. Environmental sediment samples were also collected from the lakes, for a survey of the pertinent elemental background levels. Using the obtained organisms, exposure and depuration experiments were set up at The Nakuru Municipal/L. Nakuru National Park laboratory and at The School of Biological Sciences in the University of Nairobi, Kenya. Experimental samples were stored in a freezer at -20~'C and were later dried at 80~'C, before transportation to the Aquatic Ecology Laboratory in The University of Oldenburg, Germany, for chemical and data analysis. In the chemical analysis, aliquots of 10 mg samples were digested in 2 ml safe-lock Eppendorf reaction tubes for 3 hours at 80~'C with 100 ~kl HNO3 (65% suprapure). Cadmium, Cu and Pb elements were analysed using a Varian SpectrAA 880 Zeeman instrument and a GTA 110 graphite tube atomiser. Zinc was analysed using an air-acetylene flame (Varian SpectrAA-30, deuterium background correction) and a manual micro-injection method (100 μl sample volume). All metal concentrations in biological tissues are reported in ~kg g-1 dry weight (dw). For validation of the method, Certified Reference Materials (CRMs) namely BCR-CRM No.279 Sea Lettuce (Ulva lactuca) and Standard reference Material 1572 Citrus leaves, from the Commission of The European Communities (Community Bureau of Reference), and TORT-2 Lobster hepatopancreas and CRM 278R Mussel tissue (Mytilus Edulis) from the National Research Council of Canada were analysed using the same methods. Results obtained were in agreement with the certified values at 95% confidence level. Statistical analysis and modelling were done using SYSTAT version 10 and EXCEL programmes.
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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 | 0.000 | 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) | 0.000 | 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".