Non-target screening of sediment samples fromthe Canadian Arctic: comparing two different gas chromatography – high resolution mass spectrometry (GC-HRMS) techniques
Bibliographic record
Abstract
Since the late 18th century, chemicals have been industrially produced, and used by consumers. Today, the number of registered chemicals are over 150 000 in North America and Europe alone, and the number is predicted to increase. Industrial or anthropogenic chemicals can, directly or indirectly, be released into the ecosystem during their lifetime, where they can cause harm to human health and the environment. Depending on their properties, chemicals can travel far away from its source, causing global contamination. Through this, the Arctic region becomes a sink for many different types of contaminants. Because of the danger certain chemicals pose, techniques to detect and identify them in environmental samples have evolved during recent years. In these cases, non-targeted screening methods are commonly used to characterise contaminants in samples.In this study, surface sediment samples were collected on three locations in the Hudson Bay (Canada). The samples were analysed using two different instruments: a comprehensive two-dimensional gas chromatograph coupled to a high resolution time-of-flight mass spectrometer (GC×GC-HR-ToF-MS) and a gas chromatograph coupled with a Orbitrap mass spectrometer (GC-Orbitrap-MS). After data acquisition and processing, certain components were identified in both datasets, and their semi-quantitative concentrations were calculated.Overall, 32 compounds were detected and identified in the Orbitrap dataset, and 17 of these were also detected in the GC×GC dataset. The concentration was determined semi-quantitively for the identified compounds and ranged from 0.005–333 ng/g dry weight (d.w.) for the Orbitrap dataset, and 0.013–278 ng/g d.w. for the GC×GC dataset, which was below, or in the lower half, of concentration ranges from previous studies. Overall, the data processing for Orbitrap data seems to be more advanced and evolved than for GC×GC data, causing differences between the results from the two instruments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".