Paper Citation Record · LEDGER

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields

As of 14 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2608.00593.

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

pith.paper-citation-record.v1
2608.00593 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T00:38:52.371384Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

35 of 35 outbound references displayed

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verified fuzzy26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a310c083-aab9-4a73-a47b-47d425b42376 · outbound

This paper cites Hashimoto.Abrasive Finishing Processes.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Hashimoto.Abrasive Finishing Processes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:59.803419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c4a167f9-54c4-4189-acc8-a2930f78e675 · outbound

This paper cites Mass Finishing, Inc., 2007.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Mass Finishing, Inc., 2007

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:59.601454Z

Source-reported events for the cited work

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Observation 6e3ccd40-eafd-46de-a4cd-886b2d8b79c2 · outbound

This paper cites Numerical simulation and experimental study of normal 25 force and particle speed in the robotic stream finishing process.Journal of Manufacturing Processes, 98:1–18, 2023.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Numerical simulation and experimental study of normal 25 force and particle speed in the robotic stream finishing process.Journal of Manufacturing Processes, 98:1–18, 2023

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:59.359213Z

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Observation 6bc9d4c7-a950-498a-82d6-7bc8a9a5d64b · outbound

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Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Unresolved cited work

Reference 4

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Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-05T00:38:58.820754Z

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Observation bd6d8570-3195-4c01-bc12-39555d5a30f3 · outbound

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Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-05T00:38:58.520748Z

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Observation 96ce1212-d5f8-409e-9085-674c5ebfbfc3 · outbound

This paper cites Agent-based discrete element modeling of microbial-induced carbonate precipitation.Advanced Theory and Simulations, 9(3):e02233, 2026.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Agent-based discrete element modeling of microbial-induced carbonate precipitation.Advanced Theory and Simulations, 9(3):e02233, 2026

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:58.235470Z

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Observation 2694345f-7e2b-49dd-84d6-20001467378f · outbound

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Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Unresolved cited work

Reference 8

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Observation 396dbeb9-578e-4777-8c91-1ad44e2af370 · outbound

This paper cites Calibrated and validated wear prediction for bulk material handling equipment using dem simulations.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Calibrated and validated wear prediction for bulk material handling equipment using dem simulations

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:57.735127Z

Source-reported events for the cited work

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Observation b46851a4-ea37-47bd-9072-195b81c1b036 · outbound

This paper cites Simulation of solid particle erosion wear using discrete element method: Comparison of experimental and analysis results.Particuology, 2025.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Simulation of solid particle erosion wear using discrete element method: Comparison of experimental and analysis results.Particuology, 2025

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:57.444848Z

Source-reported events for the cited work

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Observation 9a93af2b-4268-40f9-b256-03ce66b2d9b3 · outbound

This paper cites Optimization of the stream finishing process for mechanical surface treatment by numerical and experimental process analysis.CIRP Annals, 68(1):373–376, 2019.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Optimization of the stream finishing process for mechanical surface treatment by numerical and experimental process analysis.CIRP Annals, 68(1):373–376, 2019

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:57.114804Z

Source-reported events for the cited work

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Observation ddf946ec-2dab-4dcd-8038-c05305cdfa6c · outbound

This paper cites Grinding surface roughness measurement combined with simulation data and transfer learning.Advanced Theory and Simulations, 7(5):2301100, 2024.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Grinding surface roughness measurement combined with simulation data and transfer learning.Advanced Theory and Simulations, 7(5):2301100, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:56.819186Z

Source-reported events for the cited work

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Observation 161374ea-f163-4379-9879-394d17b9e583 · outbound

This paper cites Efficient global optimization of expensive black-box functions.Journal of Global Optimization, 13(4):455–492, 1998.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Efficient global optimization of expensive black-box functions.Journal of Global Optimization, 13(4):455–492, 1998

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:56.560959Z

Source-reported events for the cited work

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Observation a96ccac5-bf35-4566-8a11-19f1bde2ee11 · outbound

This paper cites Liu, and X.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Liu, and X

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:56.257444Z

Source-reported events for the cited work

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Observation 9a10232c-6598-4bfd-8fe9-bbf98254e262 · outbound

This paper cites Kriging-based design optimization of an injection molding process for a light guide plate.Journal of Mechanical Science and Technology, 24(1):97–100, 2010.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Kriging-based design optimization of an injection molding process for a light guide plate.Journal of Mechanical Science and Technology, 24(1):97–100, 2010

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:55.984272Z

Source-reported events for the cited work

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Observation 0b350bea-557b-498e-9c92-54ca3f0b7f9c · outbound

This paper cites Efficient global optimization applied to aerodynamic design of flyback booster.Journal of Spacecraft and Rockets, 44(5):1022–1030, 2007.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Efficient global optimization applied to aerodynamic design of flyback booster.Journal of Spacecraft and Rockets, 44(5):1022–1030, 2007

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:55.735586Z

Source-reported events for the cited work

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Observation 123245ad-bffb-43ed-82d9-7923f9805d8c · outbound

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Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Unresolved cited work

Reference 17

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Source-reported events for the cited work

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Observation f59889be-177b-4674-af46-4eec4d00834b · outbound

This paper cites Bayesian methods in political science.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Bayesian methods in political science

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:55.188325Z

Source-reported events for the cited work

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Observation af0bfdbf-e960-482b-848b-9dd0ebc92d40 · outbound

This paper cites Kriging-surrogate-based optimiza- tion considering expected hypervolume improvement in non-constrained many-objective test problems.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Kriging-surrogate-based optimiza- tion considering expected hypervolume improvement in non-constrained many-objective test problems

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:54.915994Z

Source-reported events for the cited work

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Observation 9c9822fe-059e-4653-857e-89e2a17c8493 · outbound

This paper cites Weight uncer- tainty in neural networks.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Weight uncer- tainty in neural networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:54.635308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f6a02f86-f5dc-4a3e-a449-059ab03fc77e · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:54.362810Z

Source-reported events for the cited work

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Observation aff5e7eb-25ac-405a-9f46-0f5cf00df497 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 22

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Observation 90d358dd-21f0-49d7-8f66-0c8d9887f70a · outbound

This paper cites Sculley, Sebastian Nowozin, Joshua V.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Sculley, Sebastian Nowozin, Joshua V

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:54.134264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c7cc8097-465b-4035-88b7-a1fd8ba23983 · outbound

This paper cites Deep Ensembles: A Loss Landscape Perspective.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Deep Ensembles: A Loss Landscape Perspective

Reference 24

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Source-reported events for the cited work

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Observation b4b1b84d-19f9-4950-b7de-e12bb021ebbb · outbound

This paper cites Mindlin and Herbert Deresiewicz.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Mindlin and Herbert Deresiewicz

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:53.889103Z

Source-reported events for the cited work

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Observation cbf242b6-6cfa-4461-a549-b8e6b810ce30 · outbound

This paper cites Dem speedup: Stiffness effects on behavior of bulk material.Particuology, 12:107–112, 2014.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Dem speedup: Stiffness effects on behavior of bulk material.Particuology, 12:107–112, 2014

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:53.669888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0eb6b695-b58f-4233-897b-d366c5920667 · outbound

This paper cites Kingma and Jimmy Ba.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Kingma and Jimmy Ba

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:53.419739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 197a870b-68d1-453b-8e50-a2dd5520b4b4 · outbound

This paper cites Active learning literature survey.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Active learning literature survey

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:53.165471Z

Source-reported events for the cited work

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Observation fcf07507-6aff-4c7b-9b3e-78ad678842ff · outbound

This paper cites Physics-guided architecture (pga) of neural networks for quantifying uncertainty in lake temperature modeling.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Physics-guided architecture (pga) of neural networks for quantifying uncertainty in lake temperature modeling

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:52.954028Z

Source-reported events for the cited work

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Observation 51a85196-a590-4f66-90f9-f95640373fb8 · outbound

This paper cites Defining a novel k-nearest neighbours approach to assess the applicability domain of a qsar model for reliable predictions.Journal of cheminformatics, 5(1):27, 2013.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Defining a novel k-nearest neighbours approach to assess the applicability domain of a qsar model for reliable predictions.Journal of cheminformatics, 5(1):27, 2013

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:52.789359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e43eeb23-1751-4cbc-8561-9350ab146a82 · outbound

This paper cites Out-of-distribution detection with deep nearest neighbors.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Out-of-distribution detection with deep nearest neighbors

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T00:38:52.076077Z

Source-reported events for the cited work

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Observation f88f6832-68eb-40b3-ba69-4d506ebc9988 · outbound

This paper cites Gonzalez.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Gonzalez

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:52.676192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3a1b4a28-cde3-49b6-863b-2e393c09f637 · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T00:38:52.276539Z

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Observation 31b730c7-0c4e-4918-8479-204fb7f1a79f · outbound

This paper cites Spearman.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Spearman

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:52.601799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c764409-0604-436a-9631-549ae8db18c6 · outbound

This paper cites Fatigue life prediction of glare composites using regression tree ensemble-based machine learning model.Advanced Theory and Simulations, 3(6):2000048, 2020.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Fatigue life prediction of glare composites using regression tree ensemble-based machine learning model.Advanced Theory and Simulations, 3(6):2000048, 2020

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T00:38:52.513435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

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