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Paper Citation Record · LEDGER

Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.01133.

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

pith.paper-citation-record.v1
2403.01133 v2

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 2026-08-10T16:30:41.555643Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T10:50:59.859066Z

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 e13b57ca-d69e-48c3-8a79-bf5a53b538b4 · inbound

Foundation Models for CPS-IoT: Opportunities and Challenges cites this paper.

Foundation Models for CPS-IoT: Opportunities and Challenges Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-10T16:30:41.555643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T16:30:41.555643Z digest=sha256:aaa237576bda55d3046198d35847f6a4b254f4645d95a30989aa2be9969fbb2c

Observation b85cf714-b8f0-428b-9228-3364f6022806 · inbound

SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions cites this paper.

SensorChat: Answering Qualitative and Quantitative Questions during Long-Term Multimodal Sensor Interactions Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-08-09T10:50:59.864323Z

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=2026-08-09T10:50:59.336540Z digest=sha256:d5b83798d153661349dc3b8eb2ca877c5351fd8cfa899f376852b5020554e640