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Record W4200603712 · doi:10.17975/sfj-2021-007

Hong Kong Student Science Project Competition 2021

2021· article· en· W4200603712 on OpenAlexvenueno aff
Sacha Zhang, Gurman Khera, Hiu Or, Hiu Li, Yiu Fung, Long Yan, Lindon Wing, Yi Lok, Carmel Happy, Yuen Tak Yu, Tsz Wong, Lok Zhang, Cheuk Y. Tang, Wai‐Sum Chan, Luk Choi, Yin Jessica, K.A. Fu, H. J. Wing, Ka Kan, Sze Seau Lee

Bibliographic record

VenueSTEM Fellowship Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
FundersInnovation and Technology Commission
KeywordsCreativityCompetition (biology)CorporationPolitical scienceScope (computer science)Public relationsManagementSociology

Abstract

fetched live from OpenAlex

The Hong Kong Student Science Project Competition (HKSSPC) promotes the interest in science and technology among youth, develops their creativity and critical thinking skills through an innov ative application of science and technology, and ignites their passions and career interests in these areas. This year, the HKSSPC was organized by The Hong Kong Federation of Youth Groups, the Education Bureau, the Hong Kong Science Museum, and the Hong Kong Science and Technology Parks Corporation. Furthermore, it was supported by the Innovation and Technology Commission and the Hong Kong Young Academic of Sciences. We extend our thanks to all these groups for making this year’s competition a success. STEM Fellowship collaborated with the HKSSPC Secretariat to provide youth from Hong Kong with the unique opportunity to submit their work in the STEM Fellowship Journal. This year’s theme was “Inspiration from Living - Innovation from Science” with an emphasis on United Nations’ Sustainable Development Goals. The broad scope of the competition allowed participants to submit their work in a variety of areas such as water pollution, nanoparticles, artificial intelligence systems, agriculture, environmental health, plastics, waste reduction, and many more. We are pleased to share the creativity and ambitious drive for research demonstrated by HKSSPC’s participants in these proceedings. We would like to congratulate every passionate individual who participated in the HKSSPC this year and wish them the best in their future STEM-related endeavours.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.373
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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