ALGAE DETECTION AND SHIP'S BALLAST WATER ANALYSIS BY A MICROFLUIDIC LAB-ON-CHIP DEVICE
Bibliographic record
Abstract
This article reports a microfluidic lab-on-chip device that can detect algae and analyze ship's ballast water treatment performance according to the standard set by the International Convention for the Control and Management of Ships’ Ballast Water and Sediments. A microfluidic differential resistive pulse sensor (RPS) was employed to detect, count, and size two algae of different sizes, a larger alga, Pseudokirchneriella subcapitata, and a smaller one, Chlorella vulgaris. The number rate of the algae flowing through the sensing gate per 2 min is a linear function of the sample concentration. A number rate-concentration correlation curve was experimentally obtained and verified, and can be used to determine the algae concentration simply by counting the number of peaks within several minutes. This lab-on-chip device described in this article is sensitive enough to detect the algae killing efficiency by electrolysis treatment to the ballast water. Also, this device can be easily operated by nonprofessionals and thus has great potentials in shipboard on-site testing for port state control.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".