Development of a simulated earthworm gut for determining bioaccessible arsenic, copper, and zinc from soil
Bibliographic record
Abstract
Soil physicochemical characteristics and contamination levels alter the bioavailability of metals to terrestrial invertebrates. Current laboratory-derived benchmark concentrations used to estimate risk do not take into account site-specific conditions, such as contaminant sequestration, and site-specific risk assessment requires a battery of time-consuming and costly toxicity tests. The development of an in vitro simulator for earthworm bioaccessibility would significantly shorten analytical time and enable site managers to focus on areas of greatest concern. The simulated earthworm gut (SEG) was developed to measure the bioaccessibility of metals in soil to earthworms by mimicking the gastrointestinal fluid composition of earthworms. Three formulations of the SEG (enzymes, microbial culture, enzymes and microbial culture) were developed and used to digest field soils from a former industrial site with varying physicochemical characteristics and contamination levels. Formulations containing enzymes released between two to 10 times more arsenic, copper, and zinc from contaminated soils compared with control and 0.01 M CaCl2 extractions. Metal concentrations in extracts from SEG formulation with microbial culture alone were not different from values for chemical extractions. The mechanism for greater bioaccessible metal concentrations from enzyme-treated soils is uncertain, but it is postulated that enzymatic digestion of soil organic matter might release sequestered metal. The relevance of these SEG results will need validation through further comparison and correlation with bioaccumulation tests, alternative chemical extraction tests, and a battery of chronic toxicity tests with invertebrates and plants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".