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Paper Citation Record · LEDGER

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models

As of 23 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2502.08062.

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

pith.paper-citation-record.v1
2502.08062 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:01:29.926780Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

28 of 28 outbound references displayed

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External citation measurements

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Outbound references

Observation 7779cb4a-dbc1-4471-9a50-887f018acbaa · outbound

This paper cites The training data set contains random arrange- ments of many small circular tethers, so the case of a long continuous tethering bar has no analogue within the training data set.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models The training data set contains random arrange- ments of many small circular tethers, so the case of a long continuous tethering bar has no analogue within the training data set

Reference 1

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Observation 4bae664a-0d7c-40cd-9bc6-3ee88c5eff36 · outbound

This paper cites CONDOR simulations were made to simulate growth of cultured tissues in the randomly created moulds for a set of randomly selected parameter values.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models CONDOR simulations were made to simulate growth of cultured tissues in the randomly created moulds for a set of randomly selected parameter values

Reference 2

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Observation 4e327e84-dd88-48de-9574-1c8d9963a59c · outbound

This paper cites Glial cells play an important role in the nervous system, and direct the growth and support of neurons.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Glial cells play an important role in the nervous system, and direct the growth and support of neurons

Reference 3

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Observation 7e0fe245-2972-450b-81be-8e2b96d744fe · outbound

This paper cites an unresolved cited work.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Unresolved cited work

Reference 4

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Observation 66339ea5-4d2f-4352-bd99-68e97c5ae707 · outbound

This paper cites [17] to compare RAPTOR predictions with experimental results for en- gineered tissue containing corneal stromal cells.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models [17] to compare RAPTOR predictions with experimental results for en- gineered tissue containing corneal stromal cells

Reference 5

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Observation 317230ba-03ef-40f5-9b39-07b239ccdf60 · outbound

This paper cites Represen- tative examples of artificial tendon and muscle tissues grown in tethered moulds can be found in Refs.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Represen- tative examples of artificial tendon and muscle tissues grown in tethered moulds can be found in Refs

Reference 6

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Observation 8880ce8b-f6df-44fc-a508-f08b1acec100 · outbound

This paper cites Grosberg, P.-L.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Grosberg, P.-L

Reference 7

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Observation 26b00ef0-1760-4318-989f-74f696510228 · outbound

This paper cites Shimizu, H.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Shimizu, H

Reference 8

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This paper cites an unresolved cited work.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Unresolved cited work

Reference 9

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Observation fa3a2b2a-bcba-47fb-a3ab-6946c18b3426 · outbound

This paper cites Werner, T.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Werner, T

Reference 10

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Observation b1972d4c-e45d-4824-9ee9-fbe3a82cb4e9 · outbound

This paper cites Bajaj, R.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Bajaj, R

Reference 11

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Observation 2902cf8f-1367-4f17-b409-6acd9d50a374 · outbound

This paper cites Ben-Arye and S.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Ben-Arye and S

Reference 12

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 93bec0b5-0b23-47f9-bee0-e944f9fba955 · outbound

This paper cites Weinhart, A.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Weinhart, A

Reference 13

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Observation 297e2367-b587-4bb6-ba0f-8ca1dda27f37 · outbound

This paper cites Jensen, C.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Jensen, C

Reference 14

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Observation 0a50cabd-1d70-455c-8e16-876df7120fc3 · outbound

This paper cites an unresolved cited work.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Unresolved cited work

Reference 15

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Observation 5e4c2036-2b7a-42c3-995c-d3ff30de9015 · outbound

This paper cites Jensen and Y.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Jensen and Y

Reference 16

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Observation 9e31ca65-b907-4842-bf0e-e83820e95ea0 · outbound

This paper cites an unresolved cited work.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Unresolved cited work

Reference 17

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Observation 67222892-27b0-467a-a683-9a22385039fb · outbound

This paper cites Andrews, H.Dickinson, and J.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Andrews, H.Dickinson, and J

Reference 18

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Observation caebd1e9-f43c-4785-a4c9-9b53a7ccad51 · outbound

This paper cites Andrews, and H.Dickinson, High- throughput design of cultured tissue moulds using a bio- physical model: optimising cell alignment, Physical Biol- ogy 20, 066006 (2023).

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Andrews, and H.Dickinson, High- throughput design of cultured tissue moulds using a bio- physical model: optimising cell alignment, Physical Biol- ogy 20, 066006 (2023)

Reference 19

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Observation 3387bf76-c0ff-4d1c-8c5b-e24d0a5f2ff7 · outbound

This paper cites Isola, J.-Y.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Isola, J.-Y

Reference 20

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Observation aab18fad-75cb-4c8f-a876-7050eaf178f1 · outbound

This paper cites O’Rourke, R.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models O’Rourke, R

Reference 21

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Observation 65bf88d4-d0d4-420b-84ad-74b3197b96d1 · outbound

This paper cites an unresolved cited work.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Unresolved cited work

Reference 22

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Observation 56f6d3a6-e97d-439d-8c60-ccd9a0440525 · outbound

This paper cites Mukhey, J.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Mukhey, J

Reference 23

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Observation 8358e6c1-843d-4f0e-b70e-ada30b38d3dc · outbound

This paper cites Kostyuk and R.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Kostyuk and R

Reference 24

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Observation 47e886df-2bfb-463b-a65c-53f9d0000304 · outbound

This paper cites Georgiou, S.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Georgiou, S

Reference 25

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This paper cites Phillips, ‘EngNT’ – engineering live neural tissue for nerve replacement, Emerg.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Phillips, ‘EngNT’ – engineering live neural tissue for nerve replacement, Emerg

Reference 26

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Observation 125b3e1a-ebd9-439c-907a-e241c89a0157 · outbound

This paper cites Garvin, J.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Garvin, J

Reference 27

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Observation 24c11f31-ba45-4ba9-8a4a-ece8479af613 · outbound

This paper cites an unresolved cited work.

Rapid prediction of organisation in engineered corneal, glial and fibroblast tissues using machine learning and biophysical models Unresolved cited work

Reference 28

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Pith citing papers

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