Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2501.16271.
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 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-31T18:32:17.250074Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation ef5f1495-eda3-453d-b299-aab6a2634b3c · inbound
Artificial Intelligence for Food Innovation cites this paper.
Artificial Intelligence for Food Innovation From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Reference 79
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T13:36:24.692395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f62dc8e0-fb00-4cf4-9170-74baaa98be27 · inbound
Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components cites this paper.
Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T18:34:17.987777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 2249da60-7368-4b68-89a6-0a6c546ed90f · inbound
Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction cites this paper.
Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction From Molecules to Mixtures: Learning Representations of Olfactory Mixture Similarity using Inductive Biases
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T18:32:17.250074Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.