Prioritizing Suitable Quality Assurance and Control Standards to Reduce Laboratory Airborne Microfibre Contamination in Sediment Samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ubiquity and distribution of microplastics, particularly microfibres, in outdoor and indoor environments makes it challenging when assessing and controlling background contamination, as atmospheric particles can be unintentionally introduced into a sample during laboratory analysis. As such, an intra-laboratory examination and literature review was completed to quantify background contamination in sediment samples, in addition to comparing reported quality assurance and control (QA/QC) protocols in 50 studies examining microplastics in sediment from 2010 to 2021. The intra-lab analysis prioritizes negative controls, placing procedural blanks in various working labs designed to prepare, process, and microscopically analyse microplastics in sediment. All four labs are subject to microfibre contamination; however, following the addition of alternative clean-air devices (microscope enclosure and HEPA air purifiers), contamination decreased by 66% in laboratory B, and 70% in laboratory C. A review of microplastic studies suggests that 82% are not including or reporting alternative clean-air devices in their QA/QC approaches. These studies are found to be at greater risk of secondary contamination, as 72% of them ranked as medium to high contamination risk. It is imperative that laboratories incorporate matrix-specific QA/QC approaches to minimize false positives and improve transparency and harmonization across studies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it