Device for automatic measurement of light pollution of the night sky
Bibliographic record
Abstract
Abstract Research on light pollution of the night sky has been carried out in Toruń since 2017. Initially, the measurements were conducted within a network of 24 points using a handheld SQM-L photometer (Unihedron, Canada). Based on these measurements, the first accurate maps of night sky pollution by artificial light in Toruń have been developed, both in seasonal and annual terms. Using the experience gained and addressing the expectations, a decision was made to construct an automatic network of mobile devices measuring light intensity at night, covering the entire city. This paper presents the technical characteristics of the constructed automatic measurement devices that make up the distributed monitoring network and the process of testing and using the devices. The implementation of this project has started in 2020. To accommodate the evolving expectations of different user groups and the observed trends in the concept of Smart Cities, especially those related to the communication between devices of the Internet of Things (IoT), LoRaWAN was selected for data transmission. The first stage involved the construction of a prototype of an automatic, portable and cost-effective device, which was subjected to months of field testing under operational conditions. The device was built using off-the-shelf electronic components and a housing that met the requirements for outdoor use. The next stage was to calibrate the device by simultaneously comparing the obtained results with measurements taken using professional SQM devices. This was followed by the preparation of 35 identical devices, which are already operating in the measurement network in the city of Toruń.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".