Bibliographic record
Abstract
Abstract The superconducting state occurs for many elements, alloys, and compounds when cooled to sufficiently low temperatures, specifically, below the critical temperature, Tc. The initial discoveries by Onnes in 1911 and 1914 were of the loss of electrical resistance at very low temperatures and of its reestablishment by applying a strong enough magnetic field or a high enough current. A further discovery by Meissner and Ochsenfeld in 1933 was that a magnetic field could be expelled from a superconducting material as it was cooled through its Tc. This perfect diamagnetic behavior coupled with perfect conductivity led to the derivation of London equations describing the superconducting state in 1935. At present, there are many known superconducting materials ranging from pure elements such as lead, that are superconducting at liquid helium temperatures, to complex ceramics such as YBaCuO7‐xoperating at liquid nitrogen temperatures. Also, uses of superconductors vary enormously ranging from sensitive magnetic field detectors to electric power cables. Certain engineered superconducting materials and composites can carry very high transport currents in high magnetic fields and much research at this time is being devoted to the development of commercial and also power utility applications. The characterization of these technological materials is a very important part of these activities. Here, we present some basic features of magnetization characterization of superconductors, in particular high temperature superconductors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".