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Record W2522472992 · doi:10.21203/rs.3.rs-2118911/v1

Low-frequency resonance in strong heterogeneity

2022· preprint· en· W2522472992 on OpenAlexaff
Yinbin Liu

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicRandom lasers and scattering media
Canadian institutionsBurnaby Hospital
Fundersnot available
KeywordsPhysicsResonance (particle physics)Anomaly (physics)Wave packetScale (ratio)ScatteringComputational physicsGeophysicsOpticsCondensed matter physics

Abstract

fetched live from OpenAlex

Abstract Multiple scattering of wave in strong heterogeneity can cause resonance-like wave anomaly where the signal exhibits low-frequency, high intensity, and slowly propagating wave packet velocity. For example, long period event in volcanic seismology and plasma oscillations in wave-particle interactions. Collective behaviour in a many-body system is thought to be the source for generating the anomaly, however the detailed mechanism is not fully understood. Here I show that the physical mechanism is associated with low- frequency resonance (LFR) in strong small-scale heterogeneity through seismic wave field modeling for bubble cloud heterogeneity and 1D heterogeneity. LFR is a kind of wave coherent scattering enhancement or emergence phenomenon. Its resonance frequency decreases with increasing heterogeneous scale, impedance contrast, or random heterogeneous scale and velocity fluctuations; its intensity diminishes with decreasing impedance contrast or increasing random heterogeneous scale and velocity fluctuations. LRF exhibits the characteristics of localized wave in space and the shape of ocean wave in time and is a ubiquitous wave phenomenon in wave physics. The concept of LFR can open up new opportunities in many aspects of science and engineering.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.376
Teacher spread0.323 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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