Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Core Building Blocks: Next Gen Geo Spatial GPT Application
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.11029.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:08:08.598235Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T22:58:34.355484Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
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 3581b596-51a9-47dc-ab37-69be2d05a5c2 · inbound
Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels cites this paper.
Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels Core Building Blocks: Next Gen Geo Spatial GPT Application
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T22:58:34.357925Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 89311b49-ab87-47d0-823f-048b16028341 · inbound
Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations cites this paper.
Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations Core Building Blocks: Next Gen Geo Spatial GPT Application
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:08:08.598235Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.