Paper Citation Record · LEDGER

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach

As of 11 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2501.11213.

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

pith.paper-citation-record.v1
2501.11213 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:34:56.254391Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

12 of 12 outbound references displayed

verified exact3
verified fuzzy6
unresolved2
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 056a36e4-2109-469b-80ac-9c1b28488833 · outbound

This paper cites A model for predicting failure of oil pipelines,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach A model for predicting failure of oil pipelines,

Reference 1

Resolution: verified exact; raw_fallback, observed 2026-08-10T18:34:56.588919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bd3dbfcc-2e73-4ee9-8d2d-14887492ffc2 · outbound

This paper cites Applications of machine learn- ing in pipeline integrity management: A state-of-the-art review,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Applications of machine learn- ing in pipeline integrity management: A state-of-the-art review,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T18:34:56.210183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d70415c-84c0-405f-91e5-623ad9d08474 · outbound

This paper cites Annual Flowline Spill Report - 2019,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Annual Flowline Spill Report - 2019,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:34:56.811355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fbd46ea7-e662-4c2b-8d34-7756abeb0793 · outbound

This paper cites Minimising Pipeline Leaks and Maximising Operational Life by Application of Machine Learning at Cooper Basin,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Minimising Pipeline Leaks and Maximising Operational Life by Application of Machine Learning at Cooper Basin,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:34:56.797240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 47f158b8-7a2e-4bc3-8649-59aa57d1817b · outbound

This paper cites Estimating Corrosion Growth Rate for Underground Pipeline: A Machine Learning Based Approach,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Estimating Corrosion Growth Rate for Underground Pipeline: A Machine Learning Based Approach,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:34:56.783231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 47e7c896-0f15-4a50-870f-53d5fd8790bf · outbound

This paper cites Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach,

Reference 6

Resolution: verified exact; doi, observed 2026-08-10T18:34:56.327853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 13bd0940-10ef-46fb-825c-fdc3f0447238 · outbound

This paper cites Development of a model for ranking field pipelines based on risk assessment in exploitation,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Development of a model for ranking field pipelines based on risk assessment in exploitation,

Reference 7

Resolution: verified exact; doi, observed 2026-08-10T18:34:56.312241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 55303edb-c9dc-44d0-883c-31568f72e215 · outbound

This paper cites Application of Probabilistic Model in Pipeline Direct Assessment,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Application of Probabilistic Model in Pipeline Direct Assessment,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:34:56.770120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e7d1a49d-4fcf-4689-87ad-d001c508d9ac · outbound

This paper cites Flowline Risk Review – Final Report,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Flowline Risk Review – Final Report,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:34:56.756046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5e4675e7-2f8e-433c-bfcd-f304feb0e490 · outbound

This paper cites A Prediction of Corrosion-Related Leakage on Distribution Pipelines via Machine Learning Method,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach A Prediction of Corrosion-Related Leakage on Distribution Pipelines via Machine Learning Method,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T18:34:56.692000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 418d2715-7981-4281-b231-0890b031c627 · outbound

This paper cites Random Forests,.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Random Forests,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T18:34:56.250430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54e5d7d1-7b9f-48bd-b4c2-f45f94c5b44e · outbound

This paper cites Hastie, R.

Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach Hastie, R

Reference 12

Resolution: malformed identifier; no resolver link, observed 2026-08-10T18:34:56.254391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.