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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".