Pith. sign in

Paper Citation Record · LEDGER

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1906.12340.

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

pith.paper-citation-record.v1
1906.12340 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-09T02:31:26.242405Z

measured 1 of 1 external citation measurements

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

Source: pith, 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

343
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7ec7f30a-3569-4b85-b55b-a302fda01102 · inbound

Toward Calibrated, Fair, and accurate Deepfake Detection cites this paper.

Toward Calibrated, Fair, and accurate Deepfake Detection Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Reference 298

Resolution
verified exact
arxiv_id, observed 2026-06-28T07:11:45.449382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-28T07:05:18.026601Z digest=sha256:1bb31ffcccbc10ca23b04ab5d580a37f00a659134628be1c9ee427ea759aa71f

Observation 3d9bdd8e-cf74-438d-a4b7-19465604e75b · inbound

Agent Delivery Engineering Predictive Reliability Framework cites this paper.

Agent Delivery Engineering Predictive Reliability Framework Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-07-09T02:35:53.668735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-07-09T02:31:26.242405Z digest=sha256:48ee042c17fa516572c93ec251b6f53f6249864747da8628865cdec50afab2dd