On Magnetic Field Screening and Trapping in Hydrogen-Rich High-Temperature Superconductors: Unpulling the Wool Over Readers’ Eyes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".