Enzyme-linked immunosorbent assay (ELISA) for the screening of dioxins in fish samples
Bibliographic record
Abstract
Dioxins are environmental contaminants that are toxic to humans. The conventional analytical method for dioxins, gas chromatography - high resolution mass spectrometry, is extremely time-consuming and expensive. Research is needed to find alternative methods that will increase sample throughput while decreasing time and costs associated with dioxin detection. Dioxins readily accumulate in fish tissue and fish are a common food source for humans. Thus, the goal of this research was to develop a screening technique for dioxins in fish samples using enzyme-linked immunosorbent assay (ELISA). Three approaches, each with a different fish sample purification method but all using ELISA detection, were undertaken. This research concluded that the approach of Florisil cleanup followed by ELISA detection (Florisil-ELISA) was suitable as a screening technique. The other two approaches, one using gel permeation chromatography (GPC-ELISA) and the other using acid silica and carbon columns (acid silica/carbon-ELISA) for fish sample cleanup, were not suitable.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".