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Paper Citation Record · LEDGER

Low-dimensional geometry learning for turbulence prediction in optimized stellarators

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2603.17366.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2603.17366 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:03:35.755630Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-11T02:37:46.415570Z

Reference resolution

0 of 0 outbound references displayed

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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation de4c3193-5052-467d-a638-7e562508a33f · inbound

Bayesian optimization of stellarator alpha-particle confinement using data-informed parameter spaces and dimensionality reduction cites this paper.

Bayesian optimization of stellarator alpha-particle confinement using data-informed parameter spaces and dimensionality reduction Low-dimensional geometry learning for turbulence prediction in optimized stellarators

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-07-04T02:59:25.701238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-26T18:41:46.639806Z digest=sha256:d925938cb9fb89d620cd8e9591b0fd3542576276d6914d818b602c04d285b3d7

Observation ea86c384-83d0-4697-8dd6-658b1fb7576c · inbound

Towards joint optimization of stellarator coils and support structures cites this paper.

Towards joint optimization of stellarator coils and support structures Low-dimensional geometry learning for turbulence prediction in optimized stellarators

Reference 51

Resolution
verified exact
local_arxiv, observed 2026-07-11T02:37:46.437286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-07-11T02:34:37.275464Z digest=sha256:440dda5b58c2f9f3c988c10a11d2436dea6adb126cdcb9f5abc2a482df533fa1

Observation f5fb6833-3cf7-41e8-b9f5-12988cb1611f · inbound

Kinetic Optimization of Magnetic Mirror Confinement: Beyond Classical Loss-Cone Theory cites this paper.

Kinetic Optimization of Magnetic Mirror Confinement: Beyond Classical Loss-Cone Theory Low-dimensional geometry learning for turbulence prediction in optimized stellarators

Reference 2026

Resolution
unresolved
no resolver link, observed 2026-08-01T15:03:35.755630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T15:03:35.755630Z digest=sha256:08cee57166474f8f62c0ed84f1877102e4c09c3d7c3d997211ad202af7c927f8