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Paper Citation Record · LEDGER

Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2307.11224.

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

pith.paper-citation-record.v1
2307.11224 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:45:24.171374Z

measured 1 of 1 external citation measurements

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

1
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 b9910965-6b44-4372-b02d-5612d97c4a04 · inbound

Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning cites this paper.

Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-24T04:33:53.593488Z

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.

source=arxiv_source observed=2026-05-24T04:29:05.113230Z digest=sha256:cc8b415477732f958be1731038a6e5195556d8c7de32d529d346b0508e55f2d1

Observation 8dc87b1e-573f-4e06-aa48-d786d1f09daf · inbound

Learning Robust Negation Text Representations cites this paper.

Learning Robust Negation Text Representations Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:45:24.171374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:45:24.171374Z digest=sha256:0f61921069078f1fe8bda9257eafa8c5dca472a8ead7acb21422eb597b05c26b