Improving estimation of phytoplankton abundance and distribution in ballast water discharges
Bibliographic record
Abstract
Abstract With the International Maritime Organization’s (IMO) International Convention for the Control and Management of Ships’ Ballast Water and Sediments now in force, determining abundance and distribution of phytoplankton inside ballast tanks is critical for successful ballast water management, particularly when assessing compliance. The relationship between the abundance and distribution of cells was examined to obtain the best representative sample of the entire phytoplankton community in ballast tanks, comparing three ballast water sampling techniques including in-line, in-tank, and Van Dorn bottle methods. Lloyd’s index, D y , and Gini index were applied to compare methods of sample collection and determine representativeness of samples and performance of sampling methods. Phytoplankton abundance trends from live microscopy counts using fluorescein diacetate (FDA) were also compared to those using a FlowCAM on preserved samples. The phytoplankton community showed a patchy distribution inside the ballast tank and this trend was observed across all voyages. The estimated marginal mean analysis showed that in hypothetical conditions (e.g., 702 m 3 of water in ballast tank and phytoplankton whole-tank abundance of 19,522 cells), the difference among the three methods was small. Conversely, statistical analysis performed on empiric abundances using a negative binomial regression model determined that the volume discharged during sampling of ballast water has an effect on the number of cells collected on a given voyage. Results of this study also confirmed that the in-line method may be a better method at collecting phytoplankton samples from ballast tanks than the in-tank or Van Dorn method, regardless of the time at which samples are collected. Finally, the number of living cells and the number of preserved cells showed similar trends for most of the voyages, despite fewer samples analyzed using FDA.
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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".