Impact of changes in analytical techniques for the measurement of polychlorinated biphenyls and organochlorine pesticides on temporal trends in herring gull eggs
Bibliographic record
Abstract
Changes in analytical approaches during the tenure of monitoring programs for organochlorine (OC) pesticides and polychlorinated biphenyls (PCBs) may affect estimates of temporal trends. We used an in-house reference material to create multiplication factors to adjust the estimates of OC pesticides and PCBs (Aroclor equivalents) in Great Lake herring gull eggs analyzed using electron capture detection (1987-1997) to be more equivalent to estimates using mass spectrometric detection (1998-2005) as well as accompanying differences in analytical procedures. We examined temporal trends in contaminant concentrations in herring gull eggs using change point regressions, to determine whether significant changes in long-term trends were associated with analytical methodology. The highest frequency of change point occurrences shifted from 1997 (when analytical methodology was altered) to 2003 after data adjustment. The explanatory power (r2) of the regressions was lower after adjustment, although only marginally so (mean r2 difference=0.04). The initial rates of decline before change points in contaminant concentrations were generally slower after the data adjustment, but after any change points the declines were not significantly different. The regression models did not change for 83.3% of the cases. The effects on the interpretation of long-term temporal trends in herring gull eggs, although not negligible, were minor relative to the magnitude of the temporal changes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".