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Paper Citation Record · LEDGER

FoodSAM: Any Food Segmentation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2308.05938.

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

pith.paper-citation-record.v1
2308.05938 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:56:24.217543Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T14:56:24.535678Z

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 0d55332a-3239-46f1-87b1-e2c7f9f21109 · inbound

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation cites this paper.

Swin-TUNA : A Novel PEFT Approach for Accurate Food Image Segmentation FoodSAM: Any Food Segmentation

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-06T14:56:24.542485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T14:56:24.217543Z digest=sha256:369de0c766936ece53f17b702f12e6d696495def5465e5cd25c7880ff725cb57

Observation 4341735a-0ebd-41e9-96a6-017dfc20aa77 · inbound

Food Image Segmentation with LLM-Derived Ingredient Labels and Multimodal Fusion cites this paper.

Food Image Segmentation with LLM-Derived Ingredient Labels and Multimodal Fusion FoodSAM: Any Food Segmentation

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-01T01:26:07.240062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T01:26:07.240062Z digest=sha256:962c6fe602e35509c84d95f20389fc9a7cb49a239c7215aa7944add652001c38