Multifaceted analysis of micro/nanoparticles in Hopedale's snow: comprehensive characterization, cellular assessments, and implications for environmental health
Bibliographic record
Abstract
This study presents an in-depth analysis of small particles in snow samples collected from six sites in Hopedale, Nunatsiavut, Newfoundland and Labrador, Canada. Eighteen samples (three per site) were processed to assess the presence, size, concentration, and potential cellular effects of particles. Samples were filtered and classified into two groups: Group A (particles <1 µm, mean size 100–200 nm) and Group B (particles >5 µm). Particle characterization was performed using dynamic light scattering and nanoparticle tracking analysis. Cellular assays were conducted to evaluate potential biological impacts. Results showed that particle size was consistent across locations while particle concentrations varied. The presence of larger particles suggested aggregation or sedimentation of smaller ones. These larger particles showed minimal cellular effects, likely due to their low concentrations. No consistent correlation was observed between particle number and cell viability. The study enhances our understanding of particulate matter in snow and highlights the importance of considering the origin and composition of particles in toxicity assessments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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 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".