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Paper Citation Record · LEDGER

Rank-N-Contrast: Learning Continuous Representations for Regression

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2210.01189.

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

pith.paper-citation-record.v1
2210.01189 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:53:41.125059Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:07:04.354040Z

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 8011e5fa-304e-4e18-8fd4-b899c411909b · inbound

Supervised Contrastive Learning for Ordinal Engagement Measurement cites this paper.

Supervised Contrastive Learning for Ordinal Engagement Measurement Rank-N-Contrast: Learning Continuous Representations for Regression

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-07T13:53:41.125059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:53:41.125059Z digest=sha256:31afe327a8bf9a8ce1fad97832e25784a06290dca7478b378f187579d4af5fbd

Observation da00f9b9-f552-4090-aa14-1872d20fe9ff · inbound

Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models cites this paper.

Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models Rank-N-Contrast: Learning Continuous Representations for Regression

Reference 2022

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T12:07:04.402414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T12:07:03.382608Z digest=sha256:ac7e8161064f862b7e42895af6df2cb1ec41753bb08e45232ad4ccdeb0b9d3a6