A comparison of data quality control protocols for atmospheric mercury speciation measurements
Bibliographic record
Abstract
Significant advances in the measurement of atmospheric mercury species have been made in the past 10 years yet limited protocols on quality control (QC) and assurance on this data have been published in the literature. Recently, considerable work has been done to develop quality control and assurance programs within North America. Environment Canada and the National Atmospheric Deposition Network (NADP) independently developed programs, RDMQ™ and AMQC, respectively, to QC atmospheric mercury speciation data (including gaseous elemental mercury (GEM), reactive gaseous mercury (RGM) and mercury associated to particles (PHg)). These 2 programs were assessed by the criteria on which the data is QCed and comparability of the final data products. Results show that the criteria used to flag data compare well within the 4 tested sites and that the number of flags for each criterion is generally comparable. The QC programs were applied to 2 distinct data sets and the final QCed data was compared. From a mid-latitude site, the final data sets compare very well and showed there to be a 0.3, 8.6 and 15% difference in the mean GEM, RGM and PHg concentrations post QC of each program. It is recommended that either the RDMQ or the AMQC programs be employed for a typical mid-latitude site. When the QC programs were applied to highly variable data, the data do not compare as well for RGM and PHg. Results showed a 2.7, 27 and 33% difference in the mean GEM, RGM and PHg concentrations, respectively, post QC of each program. It is recommended that RDMQ be used for data that is highly variable with high RGM/PHg concentrations as it allows for more manual correction over the QCed data. This investigation of 2 QC programs produced comparable data and that either of these programs can be used as standard methods for the quality control of atmospheric mercury speciation data.
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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 | 0.138 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".