Bibliographic record
Abstract
With the recent ratification of the 2004 International Convention for the Control of Ships' Ballast Water and Sediments (BWMC) (herewith “the Convention”), the need to install Ballast Water Management Systems (BWMS) onboard existing vessels is expected to grow to an $18 - $25B USD market in the coming few years. As BWMS are added to vessels, these systems will invariably affect the ballasting of ships systems and without a careful study of the dynamics of introducing both a fine mesh mechanical filter and a disinfection stage, the performance of a BWMS onboard a vessel may be compromised significantly. This paper will examine the hydrodynamic impacts of installing a ballast water management system both in the engine room and on deck, the flow dynamics required for proper operation of fine mesh, self-cleaning ballast water treatment filters, and the relative impacts to ballast flow and how these impacts may affect proper sizing of the ballast water management system. The paper will be based both on theoretical design and calculation as well as real-world experience stemming from nearly 400 installed Ultraviolet (UV)-based Ballast Water Treatment Systems (BWTS). The paper should have value for ship owners, designers, installers, and BWTS manufacturers, each of whom may have experienced variable system performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".