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Record W2178599313 · doi:10.1119/1.4935771

Simulating Gravity: Dark Matter and Gravitational Lensing in the Classroom

2015· article· en· W2178599313 on OpenAlexaff
Jes Ford, Jared B. Stang, Catherine Anderson

Bibliographic record

VenueThe Physics Teacher · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicExperimental and Theoretical Physics Studies
Canadian institutionsScience World at Telus World of ScienceUniversity of British Columbia
Fundersnot available
KeywordsDark matterPhysicsGravitationGravitational lensAstronomyWeak gravitational lensingUniverseTheoretical physicsAstrophysicsGalaxy

Abstract

fetched live from OpenAlex

Dark matter makes up most of the matter in the universe but very little of a standard introductory physics curriculum. Here we present our construction and use of a spandex sheet-style gravity simulator to qualitatively demonstrate two aspects of modern physics related to dark matter. First, we describe an activity in which students explore the dependence of orbital velocities on the central mass of a system, in a demonstration of how scientists first discovered dark matter. Second, we discuss the use of the gravity simulator as a visualization of gravitational lensing, a current astronomical technique for mapping dark matter in the sky. After providing the necessary background for the phenomena of interest, we describe our construction of the gravity simulator and detail our facilitation of these two activities. Together, these activities provide a conceptual visualization of gravitational phenomena related to indirect detection techniques for studying dark matter.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.028
GPT teacher head0.286
Teacher spread0.258 · 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 designNot applicable
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

Citations9
Published2015
Admission routes1
Has abstractyes

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