MétaCan
Menu
Back to cohort
Record W2793135670 · doi:10.3389/frym.2017.00029

Using Bright Streams to Learn about Dark Matter

2017· article· en· W2793135670 on OpenAlexaff
Wayne Ngan

Bibliographic record

VenueFrontiers for Young Minds · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCold dark matterPhysicsDark matterHot dark matterGalaxyAstrophysicsUniverseMilky WayWarm dark matterMixed dark matterScalar field dark matterAstronomyDark fluidDark matter haloCosmologyDark energyHalo

Abstract

fetched live from OpenAlex

Matter is what we call all of the “stuff” that makes up the universe. Ordinary matter, such as atoms, makes up only about 15% of all the matter in the universe. The other 85% is called “dark matter”—it is invisible but important enough to hold our galaxy, the Milky Way, together so it does not break apart. What is dark matter? For decades, astronomers have been thinking of ideas and trying to test these ideas by observations. One popular theory called “cold dark matter (CDM)” has been widely successful in explaining dark matter in the universe as a whole, but it still needs to be tested inside our own galaxy. In this study, we show how computer simulations can help us use streams of stars in the galaxy to determine whether CDM is the correct theory to explain 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 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)0.000
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 score0.085
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.247
Teacher spread0.235 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers for Young MindsSame topicGalaxies: Formation, Evolution, PhenomenaFrench-language works237,207