Sampling and Quality Assurance and Quality Control: A Guide for Scientists Investigating the Occurrence of Microplastics Across Matrices
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
Plastic pollution is a defining environmental contaminant and is considered to be one of the greatest environmental threats of the Anthropocene, with its presence documented across aquatic and terrestrial ecosystems. The majority of this plastic debris falls into the micro (1 μm-5 mm) or nano (1-1000 nm) size range and comes from primary and secondary sources. Its small size makes it cumbersome to isolate and analyze reproducibly, and its ubiquitous distribution creates numerous challenges when controlling for background contamination across matrices (e.g., sediment, tissue, water, air). Although research on microplastics represents a relatively nascent subfield, burgeoning interest in questions surrounding the fate and effects of these debris items creates a pressing need for harmonized sampling protocols and quality control approaches. For results across laboratories to be reproducible and comparable, it is imperative that guidelines based on vetted protocols be readily available to research groups, many of which are either new to plastics research or, as with any new subfield, have arrived at current approaches through a process of trial-and-error rather than in consultation with the greater scientific community. The goals of this manuscript are to (i) outline the steps necessary to conduct general as well as matrix-specific quality assurance and quality control based on sample type and associated constraints, (ii) briefly review current findings across matrices, and (iii) provide guidance for the design of sampling regimes. Specific attention is paid to the source of microplastic pollution as well as the pathway by which contamination occurs, with details provided regarding each step in the process from generating appropriate questions to sampling design and collection.
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 | 0.001 |
| 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 | 0.001 |
| 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