Catch me if you can: Comparing ballast water sampling skids to traditional net sampling
Bibliographic record
Abstract
With the recent ratification of the International Convention for the Control and Management of Ships' Ballast Water and Sediments, 2004, it will soon be necessary to assess ships for compliance with ballast water discharge standards. Sampling skids that allow the efficient collection of ballast water samples in a compact space have been developed for this purpose. We ran 22 trials on board the RV Meteor from June 4–15, 2015 to evaluate the performance of three ballast water sampling devices (traditional plankton net, Triton sampling skid, SGS sampling skid) for three organism size classes: ≥ 50 μm, ≥ 10 μm to < 50 μm, and < 10 μm. Natural sea water was run through the ballast water system and untreated samples were collected using paired sampling devices. Collected samples were analyzed in parallel by multiple analysts using several different analytic methods to quantify organism concentrations. To determine whether there were differences in the number of viable organisms collected across sampling devices, results were standardized and statistically treated to filter out other sources of variability, resulting in an outcome variable representing the mean difference in measurements that can be attributed to sampling devices. These results were tested for significance using pairwise Tukey contrasts. Differences in organism concentrations were found in 50% of comparisons between sampling skids and the plankton net for ≥ 50 μm, and ≥ 10 μm to < 50 μm size classes, with net samples containing either higher or lower densities. There were no differences for < 10 μm organisms. Future work will be required to explicitly examine the potential effects of flow velocity, sampling duration, sampled volume, and organism concentrations on sampling device performance.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".