Development and validation of a novel suspended particulate matter sampling device for analysis of particle-bound microbial communities
Bibliographic record
Abstract
Biotic and abiotic materials attachment to suspended particulate matter in aquatic systems can increase their toxicity and health impacts and has led to an increased need for consistent sampling across various compartments. Sedimentation traps and continuous flow centrifuges are the traditional tools for sampling suspended particulate matter, while manta trawls have been widely used for surface water sampling of suspended or floating microplastics. Limitations, however, exist in the cost of sampling and infrastructure needed to deploy such devices. Here we report on the construction and usage of a novel suspended particulate matter sampling device, the microParticle Obtaining Trap (mPOT). Quality control testing of the mPOT showed suspended particle recovery rates of >90% for particles 100 µm and larger, while field sampling of groundwater, lake and river water shows consistent, size-fractionated recovery of particulate matter. The mPOT is well suited to sample systems not easily accessible by boat or for particles not commonly recovered by common suspended particulate matter sampling and for collection of particles smaller than 300 µm in size.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".