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Record W4409846773 · doi:10.71044/qsurjvol820256

Using A Cloud Chamber to Measure The Energy Of An Alpha Particle Source

2025· article· en· W4409846773 on OpenAlexaff
Melanie Phillips, Cora Neave

Bibliographic record

VenueQueen s Science Undergraduate Research Journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsQueen's University
Fundersnot available
KeywordsMeasure (data warehouse)Cloud chamberAlpha (finance)Cloud computingAlpha particleParticle (ecology)PhysicsNuclear physicsEnvironmental scienceComputer scienceStatisticsGeologyMathematicsOperating systemDatabase

Abstract

fetched live from OpenAlex

We studied the energy of alpha particles emitted during the decay of a 300 source using a cloud chamber. Tracks appearing in the chamber were recorded and measured using Kinovea video analysis software for 246 events over approximately 38 minutes. In the analysis, the source is modelled as an isotropic point source and full track lengths are calculated from the projections of the full 3-dimensional tracks into the XY plane in the cloud forming region. Applying a Gaussian fit to the data, the average track length was 1.68±0.04 cm. Using a stopping power of 1.1425MeV/cm and the same conversion factor used to determine the expected path length projection, the average value for the energy of the alpha particles was calculated to be 5.25±0.2MeV, which is within two standard errors of the accepted value of 5.49MeV.1 This shows cloud chambers can be an effective method of detecting alpha particles and accurately measuring energies.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.112
GPT teacher head0.393
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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