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Record W1992234441 · doi:10.1002/pssb.200404782

Localization of electronic eigenstates and spin waves in diluted magnetic semiconductors at low carrier densities

2004· article· en· W1992234441 on OpenAlexaff
Mona Berciu, R. N. Bhatt

Bibliographic record

Venuephysica status solidi (b) · 2004
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCondensed matter physicsDopantMagnetic semiconductorMagnetic fieldCharge carrierSpin (aerodynamics)SemiconductorElectronPhysicsDopingMaterials scienceQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract Using mean‐field and random‐phase approximations, we perform numerical simulations on finite‐size systems to investigate the effects of disorder on the nature (localized or extended ) of both electronic states and collective magnetic (spin‐wave) excitations for a prototypical disordered system with both electronic (fermionic) and magnetic degrees of freedom. We use a simple impurity model appropriate for dilute magnetic semiconductors at low charge carrier concentrations, below and near the metal‐insulator transition. In our model, the positional disorder of the magnetic dopants is taken into account at the outset. We find that enhanced disorder implies significant inhomogeneity in magnetic properties, leading to appearance of localized electron states as well as localized collective magnetic excitations. As a result, positional disorder of the dopants can significantly influence transport and magnetic properties in such materials at low dopant and carrier densities. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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
Published2004
Admission routes1
Has abstractyes

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