Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Differentiable modeling to unify machine learning and physical models and advance Geosciences
As of 23 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2301.04027.
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 2026-07-22T06:31:00.163083+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-21T10:37:44.723832Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T10:40:00.665128Z
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 91731315-13ef-434b-aca4-6ccc421ab24a · inbound
A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning cites this paper.
A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning Differentiable modeling to unify machine learning and physical models and advance Geosciences
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T10:40:00.667028Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.
Observation 3c4142ce-0fd6-4e7f-a869-db23741b8922 · inbound
Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems cites this paper.
Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problems Differentiable modeling to unify machine learning and physical models and advance Geosciences
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T08:03:08.812174Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.