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Record W3013215908 · doi:10.3847/1538-4357/ab96cd

Thermally Unstable Cooling Stimulated by Uplift: The Spoiler Clusters

2020· article· en· W3013215908 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of ManitobaPerimeter InstituteUniversity of Waterloo
FundersScience and Technology Facilities Council
KeywordsGalaxyMolecular cloudStar formationRadiative coolingRadio galaxyAtmospheric pressureCooling flow

Abstract

fetched live from OpenAlex

Abstract Chandra X-ray observations are analyzed for five galaxy clusters whose atmospheric cooling times, entropy parameters, and ratios of cooling time to freefall time within the central galaxies lie below 1 Gyr, below 30 keV cm2, and between 20 ≲ min(t cool/t ff) ≲ 50, respectively. These thermodynamic properties are commonly associated with molecular clouds, bright Hα emission, and star formation in central galaxies. However, all have Hα luminosities below 1040 erg s−1 in the ACCEPT database. Star formation and molecular gas are absent at the levels seen in other central galaxies with similar atmospheric properties. Only RBS 0533 may host a radio/X-ray bubble, which are commonly observed in cooling atmospheres. Signatures of uplifted, high-metallicity atmospheric gas are absent. Their atmospheres are apparently thermodynamically stable despite the absence of strong nuclear feedback. We suggest that extended filaments of nebular emission and associate molecular clouds are absent at appreciable levels because their central radio sources have failed to lift low-entropy atmospheric gas to an altitude where the ratio of the cooling time to the freefall time falls below unity and the gas becomes thermally unstable.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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 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

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

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