Proceedings from the 2022 Global Youth Science and Technology Bowl
Bibliographic record
Abstract
The Global Youth Science and Technology Bowl (GYSTB) is an online competition based out of Hong Kong which encompasses biology, chemistry, engineering, and physics. The competition is a showcase of global youth scientific achievement and innovation, which aims to promote science and technology, provide a platform for global youth to develop their creativity and scientific mindsets, and facilitate the exchange of scientific ideas, interests, and abilities among young scientists all over the world. Young researchers worldwide compete with their research reports and prototypes. GYSTB is organized annually by The Hong Kong Federation of Youth Groups, and well supported by the academia in Hong Kong. This year, the competition received 125 teams of enrollment from 27 countries and regions including but not limited to the United States, Ireland, Sweden, South Korea, and Singapore. We extend our thanks to all parties for making this year's competition a success. STEM Fellowship collaborated with the GYSTB Secretariat to provide youth worldwide with the unique opportunity to submit their work in the STEM Fellowship Journal. This year's theme was "Sustainable Development". The broad scope of the competition allowed participants to submit their work in a variety of areas such as water pollution, food supply, medical health, energy harvest, artificial intelligence systems, environmental health, plastics, and many more. We are pleased to share the creativity and ambitious drive for research demonstrated by GYSTB's participants in these proceedings. We would like to congratulate every passionate individual who participated in the GYSTB this year and showcase their abstracts below.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.124 | 0.059 |
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".