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Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb 3 Sn wires — Europe PMC PMID:33833396

Source: Europe PMC (en)
Original: https://europepmc.org/article/MED/33833396
DOI: 10.1038/s41598-[numero omesso]-6
License (as archived): solo-consultazione
Archived: 2026-10-08T04:09:00.392Z
SHA-256 of the archived text: 3ec1ad55612ccd04864337bf173e531a28f2141e944d54dd908f8a2ed32de118
Stable URL of this page: https://metraipolis.com/documento/dc_5addeef332fc

How to cite

Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb 3 Sn wires — Europe PMC PMID:33833396 — Europe PMC (en), https://europepmc.org/article/MED/33833396 (solo-consultazione); archived copy: https://metraipolis.com/documento/dc_5addeef332fc (Metraipolis archive dc_5addeef332fc, sha256 3ec1ad55612ccd04864337bf173e531a28f2141e944d54dd908f8a2ed32de118)

BibTeX

@misc{dc5addeef332fc,
  title = {Machine learning applied to X-ray tomography as a new tool to analyze the voids in RRP Nb 3 Sn wires — Europe PMC PMID:33833396},
  howpublished = {Europe PMC (en)},
  url = {https://europepmc.org/article/MED/33833396},
  note = {License solo-consultazione; archived copy https://metraipolis.com/documento/dc_5addeef332fc; sha256 3ec1ad55612ccd04864337bf173e531a28f2141e944d54dd908f8a2ed32de118},
  year = {2026}
}

Archived text

The text is not reproduced here: the archive holds it as solo-consultazione (consultation only, no republication). Read it at the original source: https://europepmc.org/article/MED/33833396. Agents can read the archived copy with the MCP tool read_document.