Development of an automatic paddle wheel aerator and re-mote movement water quality monitoring for use in a marine shrimp farm
Bibliographic record
Abstract
A Paddle Wheel Aerator is developed in this research, which includes a wireless sensor network for measuring the water quality in the aquaculture of marine shrimp ponds.It can also move to the desired position to fill oxygen and measure water quality.A basic requirement is good standard water quality to prevent shrimp from epidemics and improve production.The water quality Paddle Wheel Aerator applies a microcontroller and sensors to measure eight parameters of water quality.These water quality parameters are observed on the web application via an IoT module.The movement system of the water-quality Paddle Wheel Aerator consists of a LiDAR, GPS, and remote RF signal.Each item of data is recorded immediately on the cloud server while the water-quality paddle wheel moves in the marine shrimp farm.The controlled Paddle Wheel Aerator is harnessed automatically to enhance precisely the spatial monitoring resolution of the measurement system installed, which is needless for a multiple measurement system with high cost of investment.Also, farmers can access real data through the Line application.Hence, they are able to plan and control a good environment for aquaculture, preventing the occurrence of various epidemics and decomposing organic matter in the pond.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".