Examining the performance of three ballast water compliance monitoring devices for quantifying live organisms in both regulated size classes (≥50 μm and ≥10–<50 μm)
Bibliographic record
Abstract
A number of ballast water compliance monitoring devices (CMDs) have been made commercially available to verify the efficacy of ballast water management systems by quantifying the living organisms for both plankton size classes (≥50 μm and ≥10-<50 μm). This study aimed to examine whether new CMDs can provide a reliable indication of compliance regarding Regulation D-2 and to evaluate their performance for indicative analysis of organisms by assessing their accuracy (comparison to microscopy) and precision (comparison within measurement). Challenge fresh water samples were collected in four locations of Lake Ontario, Canada, whereas marine challenge water samples were collected around the Bay of Fundy, New Brunswick, Canada. Ballast water samples were collected from ships visiting several ports across Canada. Overall, accuracy was higher (>80%) in estimating organisms from prepared-challenge water (Ballast Eye and BallastWISE) than from ballast water samples (>70%) (B-QUA only). The sensitivity ranged from 50 to 100% for the ≥50 μm organism size class, whereas for the ≥10-<50 μm organism size class, it was higher for freshwater samples (>75%) than for marine samples (>50%). The performance of CMDs should be assessed under real-world conditions for a better understanding and to improve their use.
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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 | 0.003 | 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".