Comparison of an Individual Congener Standard and a Technical Mixture for the Quantification of Toxaphene in Environmental Matrices by HRGC/ECNI-HRMS
Bibliographic record
Abstract
Both a technical standard and a recently commercially available standard containing 25 congeners were used to quantify toxaphene in a variety of environmental matrices, using high-resolution gas chromatography/electron capture negative ion high-resolution mass spectrometry (HRGC/ ECNI-HRMS). The purpose was to examine the differences between the two standards and to assess how well the congener standard describes the total toxaphene profile. At a resolving power of approximately 11,000 no interferences from other organochlorines were observed. Biotic matrices were enriched in octa- and nonachlorobornanes relative to the technical mixture, whereas abiotic matrices were enriched in hexa- and heptachlorobornanes. The hexa- and heptachlorobornanes were generally overestimated by the weighted response of the technical mixture, whereas the nonachlorobornanes were consistently underestimated. The extent to which the technical mixture over- or underestimates total toxaphene concentrations depends on the distribution of congeners among homologue groups and the abundance of particular congeners. The current 25-congener mixture described only approximately 35-75% of the total toxaphene response: more congeners are needed to adequately describe some matrices. Correction factors were developed that will allow laboratories to report reliable concentrations of individual congeners in samples that were quantified using the technical mixture, but they should be applied with caution, as they may be highly instrument dependent.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".