Development of a laboratory analytical method beneficial to the policies of the Canada-wide standards for dioxins and furans
Bibliographic record
Abstract
An enzyme-linked immunosorbent assay (ELISA) was tested for its ability to screen for PCDD/F in soils and sediments at 50, 1000 and 10,000 picograms toxic equivalents per gram of soil pgTEQ g-₁ (n=48, r²=0.994, slope=0.94). These results relied on two concepts developed in this thesis. The first, a congener correction factor, corrects ELISA results for differences in how ELISA and GC-HRMS calculate the dioxin content of a sample. The congener correction factor increased the correlation between ELISA and GC-HRMS TEQ values calculated using World Health Organization (WHO) toxic equivalency factors (TEF) from 83% to 94%. The correlation between ELISA and GC-HRMS TEQ values calculated using North Atlantic Treaty Organization (NATO) TEF remained strong when the correction factor was applied, falling from 102% to 94%. The second concept, a sample algorithm allows ELIAS to efficiently measure unknown PCDD/F concentrations between 30 and 10,5000 pgTEQ g-¹. The algorithm successfully placed 24 of 28 samples into their correct concentration ranges in a maximum of two ELISA each. A cost analysis of using the algorithm predicted that ELISA can screen samples three times faster than GC-HRMS while at a 60% reduction in operating cost. The success of ELISA in conjunction with its time and cost savings indicate that it can replace GC-HRMS in situations where the high precision of GC-HRMS is not required.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".