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Record W1980459935 · doi:10.1088/2041-8205/799/1/l8

THE RATE OF GAS ACCRETION ONTO BLACK HOLES DRIVES JET VELOCITY

2015· article· en· W1980459935 on OpenAlexaff
Ashley L. King, J. M. Mïller, M. F. Bietenholz, Kayhan Gültekin, M. T. Reynolds, A. J. Mioduszewski, M. P. Rupen, N. Bartel

Bibliographic record

VenueThe Astrophysical Journal Letters · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsDominion Astrophysical ObservatoryYork University
Fundersnot available
KeywordsPhysicsAstrophysicsAccretion (finance)Black hole (networking)Astrophysical jetKnot (papermaking)Active galactic nucleus

Abstract

fetched live from OpenAlex

Accreting black holes are observed to launch relativistic, collimated jets of matter and radiation. In some sources, discrete ejections have been detected with highly relativistic velocities. These particular sources typically have very high mass accretion rates, while sources lower knot velocities are predominantly associated with black holes with relatively low mass accretion rates. We quantify this behavior by examining knot velocity with respect to X-ray luminosity, a proxy for mass accretion rate onto the black hole. We find a positive correlation between the mass-scaled X-ray luminosity and jet knot velocity. In addition, we find evidence that the jet velocity is also a function of polar angle, supporting the "spine-sheath" model of jet production. Our results reveal a fundamental aspect of how accretion shapes mechanical feedback from black holes into their host environments.

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)0.003
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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