Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Code Smells for Machine Learning Applications
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2203.13746.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:17:35.387925Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T05:45:24.044035Z
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 c8097a88-ee63-4d89-8550-bb0f791874c5 · inbound
Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency cites this paper.
Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency Code Smells for Machine Learning Applications
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:17:35.387925Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ac3ad9e-4142-4e43-adea-ed6b1cd9ec4d · inbound
Specification and Detection of LLM Code Smells cites this paper.
Specification and Detection of LLM Code Smells Code Smells for Machine Learning Applications
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T15:10:20.696979Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9a53e798-6f68-43a1-a1fe-12288a7b1672 · inbound
LLM Code Smells: A Taxonomy and Detection Approach cites this paper.
LLM Code Smells: A Taxonomy and Detection Approach Code Smells for Machine Learning Applications
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T05:45:24.046547Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.