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Paper Citation Record · LEDGER

Edge Impulse: An MLOps Platform for Tiny Machine Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2212.03332.

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

pith.paper-citation-record.v1
2212.03332 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:00:34.409070Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T05:59:51.330778Z

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 d7af64eb-6331-4cf9-804e-6c355edd4705 · inbound

Navigating the Edge-Cloud Continuum: A State-of-Practice Survey cites this paper.

Navigating the Edge-Cloud Continuum: A State-of-Practice Survey Edge Impulse: An MLOps Platform for Tiny Machine Learning

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-07T15:00:34.409070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:00:34.409070Z digest=sha256:25e964238a5917c61c5efcc834883c4165fc560e65a8b1b830790758bd812348

Observation 7ccf0c99-4ce4-4e51-ac01-a64f43504ef5 · inbound

Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations) cites this paper.

Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations) Edge Impulse: An MLOps Platform for Tiny Machine Learning

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-05T05:59:51.339196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T05:59:51.028552Z digest=sha256:98dc31b12d9edae20196c060c81a28a073d991210d445a8c97abc4fac43835b3