Implementing the Model/View architecture in software of Brewer Network Spectrophotometer for long-term monitoring of UV radiation and ozone atmospheric content
Bibliographic record
Abstract
Observed recovery of the ozone layer likely caused by prohibiting some ozone-depleting substances under the Montreal Protocol is observed since 1998 but still may be unstable. In this regard, the task remains to provide the homogeneity of the ozone observations in comparison with the measurements of previous decades. One of the oldest global systems providing ozone data is the network of fully automated Brewer spectrophotometers which operates since the earlier 1980s. The existing software for control Brewer spectrophotometer was created more than 35 years ago and is in need of redeveloping to maintain long-term observations in the face of changing computer platforms. A new cross-platform control software for the Brewer is under development by a group of researchers from OIAP of RAS. This software is capable for running on computers with modern multitasking operating systems (Windows, Linux, macOS), and at the same time it has one codebase. In the new Brewer control software, the Model/View architecture is implemented to separate the code of measurement data (models) from the code of their display to the user (views). The model is responsible for data management and provides an interface for reading and writing this data, and the view retrieves the necessary data from the model, reacting to its changes, and sends the data to the user, placing them properly in the GUI of application. Models are developed in the C++ programming language using the cross-platform Qt framework, and the relational database SQLite is used as a persistent storage. This software allows improving the quality and uniformity of TOC and UV radiation measurements on the Brewer spectrophotometers of the global ozone network.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".