Automated high frequency monitoring of Lake Maggiore through <em>in situ</em> sensors: system design, field test and data quality control
Bibliographic record
Abstract
A high frequency monitoring (HFM) system for the deep subalpine lakes Maggiore, Lugano and Como is under development within the EU INTERREG project SIMILE. The HFM system is designed to i) describe often neglected but potentially relevant processes occurring on short time scale; ii) become a cost-effective source of environmental data; and iii) strengthen the coordinated management of water resources in the subalpine lake district. In this project framework, a first HFM station (LM1) consisting of a monitoring buoy was placed in Lake Maggiore. LM1 represents a pilot experience within the project, aimed at providing the practical know-how needed for the development of the whole HFM system. To increase replicability and transferability, LM1 was developed in-house, and conceived as a low-cost modular system. LM1 is presently equipped with solar panels, a weather station, and sensors for water temperature, pH, dissolved oxygen, conductivity, and chlorophyll-a. In this study, we describe the main features of LM1 (hardware and software) and the adopted Quality Assurance/Quality Control (QA/QC) procedures. To this end, we provide examples from a test period, i.e., the first 9-months of functioning of LM1. A description of the software selected as data management software for the HFM system (IstSOS) is also provided. Data gathered during the study period provided clear evidence that coupling HFM and discrete sampling for QA/QC controls is necessary to produce accurate data and to detect and correct errors, mainly because of sensor fouling and calibration drift. These results also provide essential information to develop further the HFM system and shared protocols adapted to the local environmental (i.e., large subalpine lakes) and technical (expertise availability) context. Next challenge is making HFM not only a source of previously unaffordable information, but also a cost-effective tool for environmental monitoring.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".