Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Evaluating machine learning techniques for predicting power spectra from reionization simulations
As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1811.09141.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T13:15:32.160132Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-06-28T22:22:43.245210Z
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 3d40fdc4-ee77-408f-b5e4-aff98a75408e · inbound
CosmoUiT: A Vision Transformer-UNet Hybrid for Fast and Accurate Emulation of 21-cm Maps from the Epoch of Reionization cites this paper.
CosmoUiT: A Vision Transformer-UNet Hybrid for Fast and Accurate Emulation of 21-cm Maps from the Epoch of Reionization Evaluating machine learning techniques for predicting power spectra from reionization simulations
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T13:15:32.160132Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 362485a8-7cce-40ed-af13-31407e67cb87 · inbound
21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables cites this paper.
21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables Evaluating machine learning techniques for predicting power spectra from reionization simulations
Reference 178
- Resolution
- verified exact
- local_arxiv, observed 2026-06-28T22:22:43.246839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 35a68cc5-cd9f-4fd9-b1c5-d9e121f454ee · inbound
Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization cites this paper.
Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization Evaluating machine learning techniques for predicting power spectra from reionization simulations
Reference 128
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T01:14:24.457057Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.