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Paper Citation Record · LEDGER

Bayesian Optimization for Synthetic Gene Design

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1505.01627.

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

pith.paper-citation-record.v1
1505.01627 v1

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 2026-08-19T06:32:44.657259+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:47:28.634403Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T21:44:09.480971Z

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 5b146fdb-f0e2-4c04-9862-fbb2a860f9a5 · inbound

VOPy: A Framework for Black-box Vector Optimization cites this paper.

VOPy: A Framework for Black-box Vector Optimization Bayesian Optimization for Synthetic Gene Design

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T19:32:15.492122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T19:32:15.492122Z digest=sha256:437da8f78b87236906f2cdece9500ecd4e9c86873d2b6a8a688699020b11bebb

Observation de754598-53c2-4aa9-8485-4cc6c6ee0af2 · inbound

Surrogate-Based Optimization Techniques for Process Systems Engineering cites this paper.

Surrogate-Based Optimization Techniques for Process Systems Engineering Bayesian Optimization for Synthetic Gene Design

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-11T12:41:09.699711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:41:09.699711Z digest=sha256:6f1b8b84c8bac1b29ccc1b7ab08c674e5368bd921a146fbfca0222c6ed5ee694

Observation 32da103a-c215-41d8-9aa5-5d7ce2f9c2ec · inbound

Modeling All Response Surfaces in One for Conditional Search Spaces cites this paper.

Modeling All Response Surfaces in One for Conditional Search Spaces Bayesian Optimization for Synthetic Gene Design

Reference 2016

Resolution
metadata mismatch
local_arxiv, observed 2026-08-10T21:44:09.491952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-10T21:44:09.220790Z digest=sha256:0d002df9eb531ba9fab2ec5b5694eb489a8b9db4797df7c3bb7f5426255423a5

Observation 3e8b3d6c-0fa5-49d3-9011-862bd0828d95 · inbound

Kernel Learning for Sample Constrained Black-Box Optimization cites this paper.

Kernel Learning for Sample Constrained Black-Box Optimization Bayesian Optimization for Synthetic Gene Design

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-15T17:47:28.634403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:47:28.634403Z digest=sha256:cf5099ba1296c2e48e3934c808126fbccc5384b15c85c65cbec86783a4d8ee9d