High Altitude Cosmic Radiation Measurement Using Stratospheric Balloon in Sorocaba Region – A STEM Experiment for High School Students
Bibliographic record
Abstract
This work shows the development of a platform for STEM experiments in high altitude using a stratospheric balloon, how it was developed, the challenges encountered in the whole process and the results of the first experiment carried out in this platform: cosmic radiation measurements from ground up to twenty kilometers in altitude.This platform features Globalsat GPS tracking, three cameras for recording video and still images and sensors for data collection.The onboard computer consists of Arduino and Labrador which are open source development boards with a BMP280 sensor for measuring temperature, pressure and altitude data that are stored in a mass memory card (microSD).The radiation is measured by a Geiger tube that captures alpha, beta and gamma radiation.The knowledge gathered in this experience including planning, launch, rescue and data analysis which are important to determine the onboard experiments constraints are shown and discussed.Finally, improvements concerning the payload space design and operational processes and important additional features such as telemetry and search-and-rescue aid electronics are proposed for the next launch that is scheduled for June 7, 2020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".