MétaCan
Menu
Back to cohort
Record W2529442697 · doi:10.1139/cjp-2016-0465

Studies on heavy particle radioactivity from superheavy nuclei leading to doubly magic <sup>304</sup>120 daughter nuclei

2016· article· en· W2529442697 on OpenAlexvenueno aff
K. P. Santhosh, Indu Sukumaran

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsMagic number (chemistry)Nuclear physicsNeutron numberNeutronAlpha decayMass numberCoulombSuperheavy ElementsAtomic physicsMAGIC (telescope)Neutron temperatureNeutron cross section

Abstract

fetched live from OpenAlex

The alpha decay and heavy particle radioactivity of the isotopes of even–even superheavy nuclei with Z = 122–132 have been studied within Coulomb and proximity potential model. The predicted half-lives using our model are found to be in agreement with universal formula for cluster decay of Poenaru et al., the universal decay law of Qi et al., and the scaling law of Horoi et al., and most of the estimated values are well within the experimental upper limit (T 1/2 &lt; 10 30 s). Our work targets the shell closure properties in the superheavy region. From the plots for log 10 (T 1/2 ) against the neutron number of the daughter nuclei, three prominent minima are observed at N = 178, 184, and 194. The results show that in addition to N = 184, the neutron numbers N = 178 and 194 exhibit extra stability as compared to their neighbours. Based on these important observations, we have identified the possibility of N = 194 being a magic neutron number next to N = 184. Further, a new island of stability in the superheavy region has been predicted around the doubly magic 304 120 superheavy nuclei and thus established the role of neutron shell closure in heavy particle decays very well.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.044
GPT teacher head0.294
Teacher spread0.250 · 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.

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

Citations16
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicNuclear physics research studiesFrench-language works237,207