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Structural models of FeSe<sub><i>x</i></sub>

2009· article· en· W1587411046 on OpenAlexaff
E.Z. Kurmaev, John A. McLeod, N. A. Skorikov, L. D. Finkelstein, A. Moewes, M. A. Korotin, Yu. A. Izyumov, Y L Xie, Gang Wu, X H Chen

Bibliographic record

VenueJournal of Physics Condensed Matter · 2009
Typearticle
Languageen
FieldMaterials Science
TopicIron-based superconductors research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTetragonal crystal systemStoichiometryMaterials scienceCrystallographyYield (engineering)Condensed matter physicsCrystal structureChemistryPhysicsPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Two different structural models for non-stoichiometric FeSe(x) are examined and compared with soft x-ray spectroscopy findings for FeSe(x) (x = 0.85, 0.50). A structural model of tetragonal FeSe with excess interstitial Fe gives better agreement with experiment than a structural model of tetragonal FeSe with Se vacancies. This interstitial Fe increases the number of 3d states at the Fermi level. We find evidence that large non-stoichiometric ratios of Fe:Se, such as that of FeSe(0.50), yield clusters of pure Fe in the crystal structure.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.268
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2009
Admission routes1
Has abstractyes

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