Plastic and anthropogenic microfiber pollution on exposed sandy beaches in Nova Scotia, Canada
Bibliographic record
Abstract
Aim: To investigate the baseline abundance of microplastics on two sandy beaches along an exposed coastline in an understudied region of the Northwest Atlantic. Methods: Sandy sediments were sampled from two beaches along the eastern shore of Nova Scotia, Canada from High, Mid, and Low intertidal positions. Density floatation using a sodium iodide (NaI) solution was used to separate particles from 100 g of sediments in each sample. Particles were characterized by size, shape, and colour, and Fourier transform infrared (FTIR) spectroscopy was conducted for polymer identification. Results: At both beaches, the majority of particles found were small (< 1.4 mm), transparent microfibers. Microplastics were polymers of polyethylene terephthalate (PET), nylon, or alkyds (paints). The mean concentrations at both beaches were similar, at 5.08 ± 3.20 and 5.58 ± 4.52 microplastics per 100 g of sediment. Non-plastic (i.e., natural and semi-synthetic cellulosic) microfibers were up to 19 times more abundant than microplastics, with mean concentrations of 75.9 ± 60.1 and 97.7 ± 87.9 per 100 g sediment. Mean particle counts did not differ significantly across tidal ranges due to their high variability over small spatial scales (10 s of m). Conclusion: Using new investigative tools yielded estimates of microplastic pollution 1-2 orders of magnitude lower than earlier research conducted at these sites, and was generally lower than values reported from other beaches globally. Sources of microfibers were potentially from high recreational use at these sites. Future monitoring could target these sites for time series analysis of microplastic change on exposed sandy beaches.
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 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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".