Feasibility of Establishing an Enzyme-Linked Immunosorbent Assay (ELISA) Laboratory for the Detection of Dioxins in Economically Marginalized and Developed Countries
Bibliographic record
Abstract
Finding a low cost and accessible means of detecting for dioxins in contaminated soil is a necessary step to ensure the health and safety of humans and the environment worldwide. Conventional technologies based on mass spectrometry are expensive and inaccessible. A minimally resourced laboratory and the use of ELISA will be discussed as a feasible, accessible, low cost alternative. The correlation between a minimally resourced laboratory (Ryerson University) and a fully resourced laboratory (Ontario Ministry of the Environment) was strong (n=13, r²=0.888, slope=0.87). To demonstrate the functionality of the minimally resourced laboratory, a supplemental site was characterized using ELISA. Results from the Ryerson and OMOE laboratories produced similar dioxin concentrations of undetectable to 120.26pgTEQg⁻¹ and 32.38 to 163.2pgTEQg⁻¹, respectively. This study illustrates an alternative for evaluating contaminated soil that could serve as a technology transfer for marginalized economies, and provide an accessible form of sample analysis in developed countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".