Paper Citation Record · LEDGER

Operationalizing Machine Learning: An Interview Study

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2209.09125.

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

pith.paper-citation-record.v1
2209.09125 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:55:49.315368Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:18:54.304228Z

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 99f7b894-7191-4c83-a9b2-e75e72916777 · inbound

FaaS and Furious: abstractions and differential caching for efficient data pre-processing cites this paper.

FaaS and Furious: abstractions and differential caching for efficient data pre-processing Operationalizing Machine Learning: An Interview Study

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T21:55:49.315368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cf48b76-7d20-4929-9b13-2142d4f328aa · inbound

Better Training Data Attribution via Better Inverse Hessian-Vector Products cites this paper.

Better Training Data Attribution via Better Inverse Hessian-Vector Products Operationalizing Machine Learning: An Interview Study

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T15:54:42.542396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7769c166-ff35-4c6a-b27c-0ccbba6c3aa6 · inbound

SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs cites this paper.

SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs Operationalizing Machine Learning: An Interview Study

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:41:25.622866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f749e66d-324c-4169-b762-57fba93a0103 · inbound

SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs cites this paper.

SpecDetect4ML: Detecting Non-Local ML Code Smells with Code Property Graphs Operationalizing Machine Learning: An Interview Study

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T15:17:57.694458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e9ab150-d6bd-409d-b8e0-c99107c91a2b · inbound

GPUAlert: A Zero-Instrumentation Process-Boundary Monitor for Diagnosing GPU Training-Job Failures cites this paper.

GPUAlert: A Zero-Instrumentation Process-Boundary Monitor for Diagnosing GPU Training-Job Failures Operationalizing Machine Learning: An Interview Study

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-03T19:18:54.305661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a288bd5a-f21d-4d80-b7f3-6e09d32f2a1a · inbound

Agentic Self-Healing for Data and AI Pipelines: An Affordable Vendor-Agnostic Architecture using Open-Source Software cites this paper.

Agentic Self-Healing for Data and AI Pipelines: An Affordable Vendor-Agnostic Architecture using Open-Source Software Operationalizing Machine Learning: An Interview Study

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T17:49:53.259467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.