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Record W2790141219 · doi:10.22323/1.204.0033

Production of Nickel-56 in Merger Accretion Disk

2015· article· en· W2790141219 on OpenAlexaff
Rebecca Surman, Liliana Caballero, Annelise Malkus, G. C. McLaughlin

Bibliographic record

VenueProceedings of XIII Nuclei in the Cosmos — PoS(NIC XIII) · 2015
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of Guelph
FundersU.S. Department of Energy
KeywordsNickelProduction (economics)Materials scienceAccretion (finance)AstrophysicsPhysicsMetallurgyEconomics

Abstract

fetched live from OpenAlex

Astrophysical systems that include a stellar-mass black hole surrounded by a rapidly accreting disk are potentially key contributors to galactic nucleosynthesis.Such systems can form during the merger of two compact objects, such as two neutron stars or a neutron star and a black hole, or during the core collapse of a massive, rotating star.Some of the mass ejected from these systems is expected to be processed in the accretion disk; any material from the inner regions of the disk will also be influenced by the disk neutrino emission as it streams outwards.Thus a careful accounting of the neutrino physics is required to accurately predict nucleosynthetic outcomes.Here we discuss how GR effects on the neutrino spectra and neutrino oscillations can impact the resulting nucleosynthesis.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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 designSimulation or modeling
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
Published2015
Admission routes1
Has abstractyes

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