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Interatomic Potentials for Metal/Metal Wetting Systems

2005· article· en· W2000729762 on OpenAlexaff
Takehiko Makino, Atsushi Kubo, Hiroki Iida, Shun Tanaka

Bibliographic record

VenueMaterials science forum · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsWettingMaterials scienceInteratomic potentialChemical physicsMetalSurface (topology)AlloySubstrate (aquarium)Molecular orbitalMolecular physicsMolecular dynamicsMoleculeComputational chemistryMetallurgyComposite materialChemistry

Abstract

fetched live from OpenAlex

Considering the uniqueness of wetting systems consisting of three components, namely, the surface, liquid and liquid/solid interface, it is desirable to construct interatomic potentials following a consistent policy. To investigate the physical meaning of the behavior in terms of the interatomic potentials, the wetting systems are modeled by simple two-body interatomic potentials derived using ab initio molecular orbital calculations for hypothetical clusters representing the above three components. For In and Sn liquid atoms, spreading occurs on a Cu (111) surface, while in contrast, liquid atoms penetrate the substrate and form a surface alloy in the case of a Pd (111) surface.

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)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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