Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2307.14623.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:52:02.831728Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation caa531a6-2509-4192-8fb2-1e4acb8dcc76 · inbound
EngiBench: A Framework for Data-Driven Engineering Design Research cites this paper.
EngiBench: A Framework for Data-Driven Engineering Design Research BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:52:02.831728Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8df1192f-9845-4750-b179-f031db76512a · inbound
Open Multimodal Datasets and Open-Source Software for Data-Driven Modeling of Multiphase Transport and Thermal Systems cites this paper.
Open Multimodal Datasets and Open-Source Software for Data-Driven Modeling of Multiphase Transport and Thermal Systems BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T05:35:22.518287Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a7193b17-aad6-4ee2-ac54-e41a3dc3238b · inbound
Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows cites this paper.
Neptuna: A Comprehensive Machine Learning Framework for Benchmarking Complex Multiphase Flows BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T05:17:56.992278Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.