Optimization and uncertainty assessment of a gas chromatography coupled to Orbitrap mass spectrometry method to determine organic contaminants in blood: A case study of an endangered seabird
Bibliographic record
Abstract
Birds are excellent bioindicators of environmental pollution, and blood provides information on contaminant exposure, although its analysis is challenging because of the low volumes that can be sampled. The objective of the present study was to optimize and validate a miniaturized and functional extraction and analytical method based on gas chromatography coupled to Orbitrap mass spectrometry (GCOrbitrap-MS) for the trace analysis of contaminants in avian blood. Studied compounds included 25 organochlorine pesticides (OCPs), 6 polychlorinated biphenyls (PCBs), 8 polybrominated diphenyl ethers (PBDEs) and 15 polycyclic aromatic hydrocarbons (PAHs). Four extraction and clean-up conditions were optimized and compared in terms of efficiency, accuracy, and uncertainty assessment. Extraction with hexane:dichloromethane and miniaturized Florisil pipette clean-up was the most adequate considering precision and accuracy, time, and costs, and was thereafter used to analyse 20 blood samples of a pelagic seabird, namely the Bermuda petrel (Pterodroma cahow). This species, endemic to the Northwest Atlantic, is among the most endangered seabirds of the region that in the '60 faced a decrease in the breeding success likely linked to a consistent exposure to dichloro-diphenyl-trichloroethane (DDT). Indeed, p,p'-DDE, the main DDT metabolite, was detected in all samples and ranged bewteen 1.13 and 6.87 ng/g wet weight. Other ubiquitous compounds were PCBs (ranging from 0.13 to 6.76 ng/g ww), hexachlorobenzene, and mirex, while PAHs were sporadically detected at low concentrations, and PBDEs were not present. Overall, the extraction method herein proposed allowed analysing very small blood volumes (∼ 100 µL), thus respecting ethical principles prioritising the application of less-invasive sampling protocols, fundamental when studying threatened avian species.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".