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Paper Citation Record · LEDGER

AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2308.03726.

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

pith.paper-citation-record.v1
2308.03726 v1

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 2026-08-06T19:53:26.166721Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:17:57.663500Z

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 c02fcce3-f8af-4757-96c3-09270bbb2448 · inbound

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation cites this paper.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T19:53:26.166721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:53:26.166721Z digest=sha256:093efd6a13f87c6cd0cb7eb0a4c946492202342448d61e7fb0a6cecf31414ea4

Observation 2a126709-82b4-4590-81aa-c5a471db7957 · inbound

CellNet -- Localizing Cells using Sparse and Noisy Point Annotations cites this paper.

CellNet -- Localizing Cells using Sparse and Noisy Point Annotations AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-07-03T10:17:57.664678Z

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=2026-06-27T10:08:58.211032Z digest=sha256:5295e4488a5428a0da475451fbb08fe48922c6c7b0dd61c7f9d159fada4f3a7e