Design and Application of Ultrasonic Measurement Systems for Akashiwo Sanguinea
Bibliographic record
Abstract
The red tide caused by Akashiwo sanguinea (A. sanguinea) has a serious negative impact on the ecological environment. Thus, it is of great importance to monitor the changes of algae cell concentration and thus forecast the red tide. This paper sets up two ulstrasonic systems based on the backscattered sound of algae cells, and uses them to measure the acoustic signals of A. sanguinea solutions of 17 different concentrations. The two systems were respectively developed on acoustic Doppler velocimenter (ADV) and single-probe development board (DB). The measured signals were analyzed to disclose the correlation between the integrated backscattered strength (IBS) and the algae cell concentration. The results show that both ADV system and DB system worked effectively when the algae cell concentration fell between 0.110 5 cells/L and 0.910 5 cells/L; The IBS continued to increase with the growth in algae cell concentration, but the growth rate was dampened when the concentration reached 210 5 cells/L, leading to fluctuations in the growing trend; the dampening is the result of the acoustic attenuation effect of the algae cells in medium-high concentration range. The test results of the ultrasonic systems agreed well with the theoretical predictions after particle size correction, indicating that attenuation compensation can enhance the system performance. The proposed systems can realize real-time monitoring of algae cell concentration, laying a solid basis for prewarning of red tide.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".