Reconcilable differences: The use of reference material to reduce methodological artifacts in the reporting of organochlorine pesticides and polychlorinated biphenyls
Bibliographic record
Abstract
Numerous long-term monitoring programs have assessed spatial and temporal trends of organochlorine (OC) pesticides and polychlorinated biphenyls (PCBs). Changes in analytical approaches (e.g., gas chromatography coupled with electron capture detection [GC-ECD] versus mass spectrometric detection [GC-MSD]) can reveal artifacts in the reported concentrations. In-house reference material (RM) was used to determine the analytical artifacts in the measurement of OCs and PCBs in Great Lake herring gull eggs previously analyzed from 1994 to 1996 (GC-ECD) and 1997 to 1999 (GC-MSD). Approximately 19.0% of the variability of PCB congeners in gull eggs was associated with analytical artifacts, and differences among colonies were obscured. Although the discrepancy in sum PCBs (SigmaPCBs) was fairly small (2.1%), some congeners varied considerably between methods (> 60%). After statistically removing the artifacts, only 1.4% of the variability in PCBs of herring gull eggs was associated with artifacts, and differences among gull colonies became apparent. After excluding OCs near the detection limit in the RM, statistically removing the artifacts reduced some of the differences between methods for OCs. Analytical artifacts may potentially render inferences difficult, confounded, and erroneous. When combining contaminant data obtained using different methods, the methods should be assumed to give different results unless demonstrated otherwise. Assessments of the compatibility of analytical methodologies should be made using an appropriate RM.
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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 | 0.092 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 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".