Bias from two analytical laboratories involved in a long-term air monitoring program measuring organic pollutants in the Arctic: a quality assurance/quality control assessment
Bibliographic record
Abstract
Initiated in 1992, air monitoring of organic pollutants in the Canadian Arctic provided spatial and temporal trends in support of Canada's participation in the Stockholm Convention of Persistent Organic Pollutants. The specific analytical laboratory charged with this task was changed in 2002 while field sampling protocols remained unchanged. Three rounds of intensive comparison studies were conducted in 2004, 2005, and 2008 to assess data comparability between the two laboratories. Analysis was compared for organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) in standards, blind samples of mixed standards and extracts of real air samples. Good measurement accuracy was achieved for both laboratories when standards were analyzed. Variation of measurement accuracy over time was found for some OCPs and PCBs in standards on a random and non-systematic manner. Relatively low accuracy in analyzing blind samples was likely related to the process of sample purification. Inter-laboratory measurement differences for standards (<30%) and samples (<70%) were generally less than or comparable to those reported in a previous inter-laboratory study with 21 participating laboratories. Regression analysis showed inconsistent data comparability between the two laboratories during the initial stages of the study. These inter-laboratory differences can complicate abilities to discern long-term trends of pollutants in a given sampling site. It is advisable to maintain long-term measurements with minimal changes in sample analysis.
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 | 0.104 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".