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Paper Citation Record · LEDGER

Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2305.06721.

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

pith.paper-citation-record.v1
2305.06721 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:23:12.869856Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T14:56:04.929176Z

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 42258bfd-2cf4-45aa-9288-e3c2275a8042 · inbound

Uncovering Latent Connections in Indigenous Heritage: Semantic Pipelines for Cultural Preservation in Brazil cites this paper.

Uncovering Latent Connections in Indigenous Heritage: Semantic Pipelines for Cultural Preservation in Brazil Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T10:23:12.869856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T10:23:12.869856Z digest=sha256:d382afbfe89abd28df741568afc356fbadcc9f89e6842ef594432b84720883b6

Observation 02c03515-ee4d-4e6f-9b6f-0f6cddf5fbeb · inbound

LegalBench-BR: A Benchmark for Evaluating Large Language Models on Brazilian Legal Decision Classification cites this paper.

LegalBench-BR: A Benchmark for Evaluating Large Language Models on Brazilian Legal Decision Classification Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:56:27.145980Z

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.

source=pdf_text observed=2026-05-10T04:27:23.053818Z digest=sha256:b528052c6d10170716b5b59e7b5dac7ddfb25d84349a3d60079cffa120bb5649

Observation 78b8a1e3-c6a9-4f3f-babf-e8a71a23928b · inbound

NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus cites this paper.

NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T14:56:04.932848Z

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.

source=pdf_text observed=2026-05-09T20:53:34.118813Z digest=sha256:6dec88e058305700ac1ab09b79bd6e69c96606b7cb88cb9522a54462c330ab72

Observation b22deeea-3a2e-4f6f-bd7c-4880415f56f2 · inbound

NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus cites this paper.

NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-02T15:10:40.419766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T15:10:40.419766Z digest=sha256:8c38bab1e557aca7299089c71ff7e422c4fa0b4e74393dc3398d15392018667a