Deliberative control for satellite-guided water quality monitoring
Bibliographic record
Abstract
This paper addresses the issue of efficient monitoring of Lake Winnipeg water quality by employing the power of computational intelligence methods in the processing of multi-spectral remote sensing data. The large size of Lake Winnipeg (the 10thlargest lake in the world) and its susceptibility to algal blooms makes satellite technologies indispensable in monitoring the quality of the lake's water. The remote sensing data have to be complemented by in-situ measurements due to the requirements for the calibration of satellite imagery, for precise local measurements, as well as because of the variations in water conditions. A method for the path planning of a ship equipped with water sample acquisition and processing facilities is presented. Given the complexity of the planning task (acquisition of different types of samples of different informational values, use of ancillary environment data, dependence on the results of satellite data processing, etc.), an inclusion of the deliberative level in the ship trajectory planning and control scheme is postulated. A deliberative control architecture is proposed which features a multi-model classification/regression system for the determination and forecasting of spatial distribution of water pollutants, in particular chlorophyll-a, and a cost optimizing path planner. A fuzzy system which handles different control strategies depending on the surrounding environment supervises the reactive level operational 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".