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Paper Citation Record · LEDGER

GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2207.08548.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2207.08548 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:44:14.626602Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T16:14:53.721581Z

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 f45470d0-6e71-4844-b7d0-9c4755248793 · inbound

VirnyFlow: A Design Space for Responsible Model Development cites this paper.

VirnyFlow: A Design Space for Responsible Model Development GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T11:44:14.626602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:44:14.626602Z digest=sha256:71fdc74b65a9fa62d8f78714dad168e1b87eaea9a7997dc9ab78116acf796afc

Observation d632a44d-60f9-48bb-8a28-7834124b1153 · inbound

Evaluating Deep Learning Models for Multiclass Classification of LIGO Gravitational-Wave Glitches cites this paper.

Evaluating Deep Learning Models for Multiclass Classification of LIGO Gravitational-Wave Glitches GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:40:59.479806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T17:04:42.169717Z digest=sha256:ba0dd65c474841a1590dd7df01454216b8024af74cc8eff01101b0089ff0428f

Observation 2b908582-4885-4583-92ee-b5d01d1d2a6a · inbound

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance cites this paper.

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

Reference 36

Resolution
malformed identifier
arxiv_id, observed 2026-05-25T04:40:23.578356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-25T04:39:07.498461Z digest=sha256:49190e0d2ac9df4cb1ba080a7eda31d5e43ea680bf1b9febfd62274407d78c0f

Observation 68845f5a-8161-4791-93f9-c34c344926c9 · inbound

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance cites this paper.

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance GANDALF: Gated Adaptive Network for Deep Automated Learning of Features

Reference 36

Resolution
malformed identifier
arxiv_id, observed 2026-06-30T16:14:53.723014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T16:05:21.900148Z digest=sha256:25c01890ff8374fe36cf1ae732058761cbd6d8d8da5ff9e6f47c7440bfe2b199