Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1706.06689.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:06:05.232282Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T00:23:06.242524Z
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 2db564a5-762d-4193-b082-61816e1050df · inbound
ChemMLLM: Chemical Multimodal Large Language Model cites this paper.
ChemMLLM: Chemical Multimodal Large Language Model Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:06:05.232282Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b695dd7c-6921-4828-9f9f-8149bb41d8f7 · inbound
CROP: Integrating Topological and Spatial Structures via Cross-View Prefixes for Molecular LLMs cites this paper.
CROP: Integrating Topological and Spatial Structures via Cross-View Prefixes for Molecular LLMs Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T22:32:41.162605Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81bd6f5c-73ac-4302-8235-7573d381c356 · inbound
Generic Vision and Cross-Attention for Reaction Yield Prediction cites this paper.
Generic Vision and Cross-Attention for Reaction Yield Prediction Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T00:23:06.248045Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.