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Record W4387311910 · doi:10.1007/s10948-023-06622-4

On Magnetic Field Screening and Trapping in Hydrogen-Rich High-Temperature Superconductors: Unpulling the Wool Over Readers’ Eyes

2023· article· en· W4387311910 on OpenAlexaff
J. E. Hirsch, F. Marsiglio

Bibliographic record

VenueJournal of Superconductivity and Novel Magnetism · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDiamagnetismSuperconductivityCondensed matter physicsRoom-temperature superconductorPhysicsHigh-temperature superconductivityMagnetic fieldQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract In Minkov et al. (Nat. Commun. 13:3194, 2022), Minkov et al. reported magnetization measurements on hydrides under pressure that claimed to find a diamagnetic signal below a critical temperature demonstrating the existence of superconductivity. Here, we present an analysis of raw data recently released (Minkov et al. Nat. Commun. 14:5322, 2023) by the authors of Minkov et al. (Nat. Commun. 13:3194, 2022) that shows that the measured data do not support their claim that the samples exhibit a diamagnetic response indicative of superconductivity. We also point out that Minkov et al. (Nat. Commun. 13:3194, 2022) in its original form omitted essential information that resulted in presentation of a distorted picture of reality, and that important information on transformations performed on measured data remains undisclosed. Our analysis also calls into question the conclusions of Minkov et al.’s trapped flux experiments reported in Minkov et al. (Nat. Phys. 19:1293–1300, 2023) as supporting superconductivity in these materials. This work together with earlier work implies that there is no magnetic evidence for the existence of high temperature superconductivity in hydrides under pressure.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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 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

Citations12
Published2023
Admission routes1
Has abstractyes

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