Principles and techniques towards successful development of enzyme-linked immunosorbent assay (ELISA) for dioxin analysis
Bibliographic record
Abstract
Dioxins are highly toxic, persistent and bio-accumulative compounds. Laboratory detection of dioxins in various environmental matrices is one of the most technically demanding and expensive tasks in analytical chemistry.The cost to analyze a soil sample by conventional gas chromatography-high resolution mass spectrometry (GC-HRMS) is approximately $1,900 USC accordign to the Unisted States Environmental Protection Agency (US EPA) (Billets, 2005). As an alternative, enzyme-linked immunosorbent assay (ELISA) for dioxin analysis has been commercially available for over a decade and recognized as the US EPA Method 3025. However, assay attributes need to be examined, especially at trace level detection. In this study, sources of error in ELISA, such as background contamination and dioxin-like polycholrinated biphenyl (dl-BCB) cross-reactions have been investigated. Quality assurance data on spikes have been reviewed and the recovery was estimated to be 70%. Technical details that are crucial for the performance of dioxin in ELISA were also identified and addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".