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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".