Propose an Automatic Ammonia Concentration of Water Measuring System Combining Image Processing for Aquaculture
Bibliographic record
Abstract
The underwater environmental monitoring system applying IoT is now developing for 4.0 industry. It helps not only to simplify work but also to increase efficiency and reduce costs and execution time as well as ensure health to avoid contact with toxic solutions. In this paper, we first model the process of measuring NH3 concentration manually to automate. Secondly, the proposed model is combined with the process of processing output image automatically and displaying the results on the server. Thirdly, the system is able to measure the concentration by VNC viewer connecting with Raspberry pi4 via WiFi. The result of the NH3 concentration parameter is sent to users quickly to alert promptly when it reaches a dangerous threshold. Experiments show that the system not only ensures fast processing time (less than 5 minutes) but also improves accuracy (up to 80%) comparing with real devices.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".