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Record W4408187138 · doi:10.1099/mic.0.001538

Development and validation of a novel suspended particulate matter sampling device for analysis of particle-bound microbial communities

2025· article· en· W4408187138 on OpenAlexaff
Fuad J. Shatara, Kiyoko Yokota, Justin Peschman, Jacob Schoville, Liyuan Hou, R. Preston Withington, Cole F. Beale, Mark Pelusi, Kyle Boldon, Hannah Nicklay, Michael R. Twiss, Charles J. Paradis, Erica L.‐W. Majumder

Bibliographic record

VenueMicrobiology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsAlgoma University
FundersGreat Lakes Research Consortium
KeywordsParticulatesEnvironmental scienceParticle (ecology)Sampling (signal processing)Environmental chemistryChemistryBiologyEcologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.267
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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