MétaCan
Menu
Back to cohort
Record W2986398673 · doi:10.1063/1.5126600

Spin number dependent dissipative coupling strength

2019· article· en· W2986398673 on OpenAlexafffund
Chenhui Yu, Ying Yang, Jinwei Rao, Paul Hyde, Yi‐Pu Wang, Bo Zhang, Y. S. Gui, C.‐M. Hu

Bibliographic record

VenueAIP Advances · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsDissipative systemYttrium iron garnetCoupling (piping)Condensed matter physicsCoupling strengthPhysicsScalingCavity wallMaterials scienceQuantum mechanicsGeometryComposite material

Abstract

fetched live from OpenAlex

A system consisting of a yttrium iron garnet (YIG) sphere coupled to a 1D circular-rectangular cavity is tuned between level repulsion and attraction by rotating the angular position of the YIG sphere within the cavity. The dominance of coherent or dissipative coupling mechanisms was determined, and the coupling strength was deduced by fitting the transmission spectra. By changing the diameter of the YIG sphere from 0.5 mm to 0.3 mm and 1.0 mm, we confirm that the N scaling of coherent coupling strength is also applicable in dissipative coupling. A large YIG sphere leads to an enhanced coupling strength that is useful for information processing. Alternatively, a small YIG sphere results in a narrow transition regime, which may be helpful for identifying the dissipative coupling dominated regime and providing insight into the physical origin of dissipative coupling.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.279
Teacher spread0.272 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueAIP AdvancesSame topicMechanical and Optical ResonatorsFrench-language works237,207