Development of a mobile app for early warnings in aquaculture farms: Identification of end-user needs
Bibliographic record
Abstract
Aquaculture has been the fastest-growing food production sector for decades. Nowadays, it is directed towards economically, socially, and environmentally acceptable production. Mariculture production begins at the sea, in cages, where fish is being kept until the appropriate market weight and size is achieved. There are different ways how it can affect the marine environment. Fish products, feces, medicine, food, and other factors have an impact on the sea quality parameters. Marine pollution considers the presence of undesirable materials in the ocean environment that affect mariculture, affecting the seawater's physical, chemical, and biological conditions. To help prevent and to monitor marine pollution, sensors connected with artificial intelligence, Internet-of-Things, applications, and other software are used. The use of multiparameter probes with different sensors in the sea enables the detection of changes in real-time and instantaneous warning. A mobile application connected to the probe provides information about parameters like salinity, sea temperature, oxygen, depth, pH etc. The goal of the developed mobile application is to prevent more serious marine pollution and impact on the production and environment by monitoring sea conditions, reacting on time in the case of anomalies, recognizing unusual patterns in the parameter value distribution from the existing data, as well as expanding the circle of people who can notice and report possible pollution. An early warning algorithm is being developed based on normal, abnormal, and reference values. This work introduces the development of a mobile app, with the emphasis on the basic requirements of the end-user.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".