Interlaboratory evaluation of the assessment of arsenic bioaccumulation from field collected sediments using <i>Hexagenia</i> spp
Bibliographic record
Abstract
Standardized bioaccumulation testing of aquatic organisms is essential to understanding the impact of historical contamination on the quality of water and sediment. A standardized 28-d laboratory bioaccumulation method with a freshwater burrowing mayfly, Hexagenia spp., has been developed and internally validated by the Ontario Ministry of the Environment and Climate Change (MOECC). An interlaboratory comparison was conducted to assess the precision of this method. Field-collected sediment contaminated with arsenic was chosen for the present study. Control and test sediments were subsampled and sent to 6 laboratories to perform the bioaccumulation test. One laboratory failed to meet the control survival criterion of ≥80%. When results of this laboratory are removed from the arsenic accumulation assessment, the mean interlaboratory variability (expressed as coefficient of variation) of the arsenic whole-body concentration is reduced from 44% to 24% in the test sediment-exposed Hexagenia spp. There was no significant interlaboratory difference between the Hexagenia spp. arsenic accumulations. While improved culturing and organism holding guidance may increase laboratory success, the MOECC Hexagenia spp. bioaccumulation test method has tight biological method precision when the control survival criterion is met. Environ Toxicol Chem 2016;35:2448-2455. © 2016 SETAC.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".