Analysis of Phytoplankton and Microplastics from ECOA-3 Cruise in the Gulf of Maine Using Flow Imaging Microscopy
Bibliographic record
Abstract
The focus of this study was to determine the distribution and abundances of potential microplastics in the Gulf of Maine, while also looking at diatom and ceratium abundances and distributions from an ECOA-3 cruise in August 2022 from Nova Scotia to New Hampshire. The abundance and distribution of microplastics in the ocean is currently being studied and ambiguous, it is a new and emerging field in Oceanography. The data was gathered using a FlowCam, a flow-through imaging microscope that captures images of particles. The data was then analyzed using VisualSpreadsheet® a data analysis software program, and then visually represented with Ocean Data View to display the distributions. The data showed that there was a higher abundance of potential microplastics at all three depths (surface, 15m, 30m) besides that of Skeletonema (1 of 5 diatom species studied) and that the abundance of potential microplastics and phytoplankton decreases with increased depth. It also showed there was a higher overall abundance of potential microplastics and phytoplankton in the Gulf of Maine compared to Nova Scotia. Overall, the abundance, distribution, and identification of microplastics in the ocean are new and not well documented, so data was taken from the ECOA-3 cruise to comprehend potential microplastics, ceratium, and diatoms in the Gulf of Maine and Nova Scotia marine areas.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 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".