Paper Citation Record · LEDGER

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm

As of 17 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2507.02072.

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

pith.paper-citation-record.v1
2507.02072 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:45:30.673692Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact11
verified fuzzy1
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a5006048-2b9e-4ae0-897a-b94664354769 · outbound

This paper cites Beaumont, M.A.,.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Beaumont, M.A.,

Reference 6

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.855811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 12713bf9-8886-4423-874b-54b85ba3b77b · outbound

This paper cites 11 Figure 1: (A) Simulated epidemic based on SIR model (black curve) and observed data (red dots).

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm 11 Figure 1: (A) Simulated epidemic based on SIR model (black curve) and observed data (red dots)

Reference 7

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.711482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5bfac3ee-eafd-4f9d-8b41-673bef482bfc · outbound

This paper cites Katono, K., Alicai, T., Baguma, Y., Edema, R., Bua, A., Omongo, C.A.,.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Katono, K., Alicai, T., Baguma, Y., Edema, R., Bua, A., Omongo, C.A.,

Reference 13

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.791371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6ce8f9c6-0372-49ed-b451-f538fd1903dd · outbound

This paper cites Szyniszewska, A.M.,.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Szyniszewska, A.M.,

Reference 15

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.726138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6fcfa0eb-2cc3-43d5-a82e-6730926e5377 · outbound

This paper cites Epidemics 29, 100368.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Epidemics 29, 100368

Reference 16

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:45:31.087897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5913a97d-592e-4fed-a22d-2977a6ef2fe4 · outbound

This paper cites Retkute, R., Touloupou, P., Bas´ a˜ nez, M.G., Hollingsworth, T.D., Spencer, S.E.F.,.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Retkute, R., Touloupou, P., Bas´ a˜ nez, M.G., Hollingsworth, T.D., Spencer, S.E.F.,

Reference 17

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T20:45:31.003634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 337528ba-dacf-400d-b289-4e3a001db1dc · outbound

This paper cites (B) The model host landscape, representing the fraction of 1 km2 grid cell occupied by cassava, derived from Cas- savaMap (Szyniszewska, 2020).

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm (B) The model host landscape, representing the fraction of 1 km2 grid cell occupied by cassava, derived from Cas- savaMap (Szyniszewska, 2020)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:45:31.228879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f1494842-266b-41cb-a299-746a89ab5278 · outbound

This paper cites Journal of Computational Physics 22, 403–434.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Journal of Computational Physics 22, 403–434

Reference 1976

Resolution: unresolved; no resolver link, observed 2026-08-06T20:45:30.613955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39c16fc7-0c86-4641-a214-73c318e5630e · outbound

This paper cites The Jour- nal of Physical Chemistry 81, 2340–2361.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm The Jour- nal of Physical Chemistry 81, 2340–2361

Reference 1977

Resolution: unresolved; no resolver link, observed 2026-08-06T20:45:30.618842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 891869e6-86dd-429a-99c0-408e79d3ab9c · outbound

This paper cites Proceedings of the National Academy of Sciences 100, 15324–15328.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Proceedings of the National Academy of Sciences 100, 15324–15328

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-06T20:45:30.637359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60c2be35-f5bd-4c47-8b7e-2f01386cf0c3 · outbound

This paper cites Journal of Phytopathology 153, 307–312.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Journal of Phytopathology 153, 307–312

Reference 2005

Resolution: verified exact; raw_fallback, observed 2026-08-06T20:45:31.179898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cd402b7b-3191-4810-b830-cce333916bb5 · outbound

This paper cites Plant Disease 91, 24–29.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Plant Disease 91, 24–29

Reference 2007

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.869498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a92a8ad-8364-4a85-bdce-e1466e3ab68e · outbound

This paper cites Annual Review of Ecology, Evolution, and Systematics 41, 379–406.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Annual Review of Ecology, Evolution, and Systematics 41, 379–406

Reference 2010

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.842246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fefeff6c-0c1c-4a49-ab01-8e606649d4ae · outbound

This paper cites Plant Pathology 65, 299–309.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Plant Pathology 65, 299–309

Reference 2015

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.740890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e2117c36-3f75-4dbb-81cb-65c40b032651 · outbound

This paper cites Systematic Biology , syw077URL: http://dx.doi.org/10.1093/sysbio/syw077, doi:10.1093/sysbio/syw077.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Systematic Biology , syw077URL: http://dx.doi.org/10.1093/sysbio/syw077, doi:10.1093/sysbio/syw077

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T20:45:30.633211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29ca9de4-cf88-4cde-84e7-ed886ec0f344 · outbound

This paper cites PLOS Computational Biology 13, e1005654.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm PLOS Computational Biology 13, e1005654

Reference 2017

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.755469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a49ce660-4c81-4f57-837e-d6da4f97f2fa · outbound

This paper cites an unresolved cited work.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:45:31.248599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1bcb02ee-6df9-47a2-b8e9-3deb9c4263af · outbound

This paper cites Nature Methods 17, 455–456.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Nature Methods 17, 455–456

Reference 2020

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.827588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae3b67bd-4818-489e-a30d-6a58cdc93be6 · outbound

This paper cites Heliyon 9, e19939.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Heliyon 9, e19939

Reference 2023

Resolution: verified exact; doi, observed 2026-08-06T20:45:30.884022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 282177c0-f86a-4454-93f3-7c3102f1ee19 · outbound

This paper cites Advances in Approximate Bayesian Inference for Models in Epidemiology.

A novel two-stage parameter estimation framework integrating Approximate Bayesian Computation and Machine Learning: The ABC-RF-rejection algorithm Advances in Approximate Bayesian Inference for Models in Epidemiology

Reference 2025

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:45:31.207313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.