On the development of a cross-platform database application for storing long-term observations of ultraviolet radiation and total ozone content obtained using Brewer Spectrophotometer
Bibliographic record
Abstract
Reducing emissions of some ozone-depleting substances (ODSs) entails uneven recovery of the ozone layer as it is also affected by ODSs emissions that not restricted by the Montreal Protocol. In this regard, there remains a need to continue monitoring the total ozone content (TOC) and ultraviolet (UV) radiation, as well as to ensure the uniformity of the measurements obtained in comparison with the data for previous decades. The network of Brewer spectrophotometers, operating since the early 1980s, is one of the oldest global systems that provides high-accuracy TOC and spectral UV radiation data. A new cross-platform database application for storing measurements, internal tests and instrumental constants of Brewer spectrophotometers is under development by A.M. Obukhov Institute of Atmospheric Physics of RAS. This application is capable of running on the computers with Windows, Linux, or macOS and has one code base. The database application of the Brewer measurements is an ASP.NET Core MVC web application with a cross-platform embedded SQLite DBMS as persistent storage. The Brewer measurements database itself is implemented as a .NET Standard shared library in the C# programming language using Entity Framework (EF) Core as an object-relational mapping tool. The application represents data in a hierarchical or relational manner, with the data rationalized into discrete entity classes. All entities are mapped to their database tables with columns corresponding to the properties of the entity classes. Proposed the database application will allow maintaining the integrity and consistency of TOC and UV measurements on Brewer spectrophotometers of the global 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".