Toxicological Approach for Assessing the Heavy Metal Binding Capacity of Soils
Bibliographic record
Abstract
A toxicological approach was taken to determine the heavy metal binding capacity of soils. A soil heavy metal binding capacity (SHMBC) methodology was developed and was based on the use of the MetPLATE TM toxicity test kit, a bioassay that is specific for heavy metal toxicity. SHMBC test is based on the heavy metal binding capacity (HMBC) concept that has been considered in the assessment of the metal binding capacity of surface waters (Huang et al., 1999 Huang, F., Bitton, G. and Kong, I.-C. 1999. Determination of the heavy metal binding capacity of aquatic samples using MetPLATE™: a preliminary study. Sci. Total Environ, 234: 139–145. [Crossref], [PubMed] , [Google Scholar]) and solid wastes landfill leachates (Ward et al., 2005 Ward, M., Bitton, G. and Townsend, T. 2005. Heavy metal binding capacity (HMBC) of municipal solid waste landfill leachates. Chemosphere, 60: 206–215. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). SHMBC is the ratio of the EC 50 of an added metal in a soil sample divided by the EC 50 of a metal in a reference soil (clean Ottawa sand). A higher SHMBC value indicates higher metal binding to soil and lower bioavailability and potential toxicity to the test bacteria. Five soils (two sandy soils, two organic soils and a clay soil) were used to determine their binding capacity towards Cu, Zn, and Hg, using the developed SHMBC test. The test measured the ability of the solids to reduce metal bioavailability and toxicity. SHMBC was highest for the clay soil and lowest for the sandy soils. The potential application of this relatively rapid (a few hours) test to predict metal toxicity to terrestrial plants is discussed.
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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 | 0.003 | 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 | 0.001 |
| 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 teacher head, 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".