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Paper Citation Record · LEDGER

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution

As of 18 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:1909.01725.

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

pith.paper-citation-record.v1
1909.01725 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:17:29.596158Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

53 of 53 outbound references displayed

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  • verified fuzzy24
  • unresolved28
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  • malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 36b968c9-ed61-46eb-984b-013affa9c6c2 · outbound

This paper cites Finally, in Sec.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Finally, in Sec

Reference 1

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Observation a8b6d2f6-a132-453d-9026-916c8aef2118 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 2

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Observation 6b16bc70-b29d-4fe3-83dc-bc92c26251ee · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 3

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This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 4

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Observation 74d477c9-cfa9-4984-977e-2ba283788b9c · outbound

This paper cites In this case, noting that F−1(y) = exp ( m + √ 2s erf−1(2y− 1) ) (Table I), and that Φ(xG) is given in Eq.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution In this case, noting that F−1(y) = exp ( m + √ 2s erf−1(2y− 1) ) (Table I), and that Φ(xG) is given in Eq

Reference 5

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This paper cites However, there is no analytical solution of the integral in Eq.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution However, there is no analytical solution of the integral in Eq

Reference 6

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Observation c7cde9e2-5d6b-4daa-8afb-4da715149532 · outbound

This paper cites As can be seen in Table II, the standardized forms of the Weibull and Pareto distributions have shape parameters, δ and ε respectively, which control the tail behavior.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution As can be seen in Table II, the standardized forms of the Weibull and Pareto distributions have shape parameters, δ and ε respectively, which control the tail behavior

Reference 7

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Observation 90d18f1d-bdfb-4221-b496-c32d10372adb · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 8

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Observation ad5f0221-4a93-4cd1-92f4-b2a243932246 · outbound

This paper cites Likenkaer-Hansen, V.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Likenkaer-Hansen, V

Reference 9

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This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 10

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Observation 94a20628-4999-44a2-92fb-3f4cafb12e20 · outbound

This paper cites Duarte & V.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Duarte & V

Reference 11

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This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 12

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 13

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Observation 6524b01e-37eb-4080-b55b-dca28c7d361f · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 14

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Observation 314cb06e-36b8-4117-acd9-6e143294502a · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 15

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Observation 58154747-7583-4d96-b78d-78d48f95bb67 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 16

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Observation 81159177-5f36-48a7-8ea4-62f5922c7fdd · outbound

This paper cites Lovejoy and B.B.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Lovejoy and B.B

Reference 17

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Observation d8bbac28-d136-4e69-832f-e302b42aae00 · outbound

This paper cites Bartos and I .M.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Bartos and I .M

Reference 18

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 19

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Observation cff3ecee-cdee-4635-82c0-fd17ba79dc13 · outbound

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 20

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 21

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Observation afb8d357-b986-4b18-b851-f6f9b3965a19 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 22

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Observation fa2f71e5-5250-4e3e-8b5f-0ad83e1bbb00 · outbound

This paper cites Bernaola-Galvan, J.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Bernaola-Galvan, J

Reference 23

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Observation 9a6b2a37-2e24-4b89-be88-d8a95d0defdf · outbound

This paper cites Theiler, S.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Theiler, S

Reference 24

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Observation 71a59050-da24-4850-a6c8-8e27a04c5986 · outbound

This paper cites Schreiber and A.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Schreiber and A

Reference 25

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kugiumtzis

Reference 26

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 27

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 28

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Observation 1d23446f-b01d-4174-97cf-3643e2b5770e · outbound

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Press, S.A

Reference 29

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 30

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Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 31

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This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 32

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This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 33

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This paper cites Kugiumtzis and E.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kugiumtzis and E

Reference 34

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Observation 658a5abf-feba-4938-91d2-af84b6dabb04 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.511268Z digest=sha256:01da0a9faca7d778add4231b57e25dfe4cbd7de058abf6c16072d4c99972d3b1

Observation 9fe4503d-8941-405c-9992-ccec1d48e6ed · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.901681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.515566Z digest=sha256:8d21dbb84cb85b827c91329304cccd49bda6cfc10c03fd3a0f7c661a71f6459a

Observation bbe2d041-d98d-4b51-a946-05b39f1a9ef4 · outbound

This paper cites Ng and M.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Ng and M

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.885265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.520371Z digest=sha256:48bb790365b30c6da753c9690310a21a8ae8ea6c868883f2c4a47b456f2161a4

Observation a159aa1e-e4ff-45be-96b0-35c3a592a6e6 · outbound

This paper cites Kugiumtzis.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kugiumtzis

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.869573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.524888Z digest=sha256:4b64be78d5c6b4ffde9d435f5792a80099b1e6f82c3e41afebe45ef5f3f7bf2c

Observation dccccb61-3f99-40fe-a6b9-d8bd12712f1d · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 39

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.854604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.529443Z digest=sha256:509b7eb289d0af54efe94cd0047f1870b8f846669c71bb341fd30b7d0296fcda

Observation 3305738d-38b4-454c-b776-d659e4f7eb60 · outbound

This paper cites Statistics for Long-Memory Processes, (Chapman and Hall/CRC, 1998).

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Statistics for Long-Memory Processes, (Chapman and Hall/CRC, 1998)

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.840654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.533845Z digest=sha256:488ac3a36dad7cb7b6995fc703f94f150e4254c13c8433af165c6c3b432666ca

Observation 7227c845-4a8e-466e-8fdb-eaa5d0287ebe · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.825524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.538445Z digest=sha256:e5c3bf58d8a069838ad3f74fd25014efefe8fd8171a3d7e2af39c64ac2fe0f99

Observation a5a8b643-2b9d-4e3b-8a53-18a7b08124e0 · outbound

This paper cites Carretero-Campos, P.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Carretero-Campos, P

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.810529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.543631Z digest=sha256:662a47834979e8c3e008a0d6597cf44a115e0c6dd15d308e03682d5f37b4116f

Observation a68ddd4a-69aa-43ac-9022-761501bdcad8 · outbound

This paper cites Kalisky, Y.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kalisky, Y

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.795392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.548041Z digest=sha256:5669ddf62e87482041084f0ff3e051750c22df20826478dd28418136b0633db0

Observation e784c22c-bf39-400c-9bdc-aa2b1b2d7e30 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.779155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.552355Z digest=sha256:4dc1d02d6ff03e6925e1fffd30b0e4f9d54c7f36c01f7d9d0cd6ad08cc585f08

Observation 3238f358-ad78-4b74-846c-20828eee8bdb · outbound

This paper cites G´ omez-Extremera, P.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution G´ omez-Extremera, P

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.762882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.557057Z digest=sha256:be6960069442bc533ce56db9d1c6d30191db835839a50c4f2dcc1b08418ff2ca

Observation ac07f20a-d269-460d-95a2-8cc96ba36c6e · outbound

This paper cites Carpena, M.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Carpena, M

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.748191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.562273Z digest=sha256:677bc90ed4302e827e9fc3a2a8824315591bc0d2ad331257928195522bea366a

Observation 02b4c08d-b223-4b92-a43c-e0707e2e8c48 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 47

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.733338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.566840Z digest=sha256:371267bc472ddaff6a111f134b95a67ecd7e98804c6f0bdfc0b511819b5a38dd

Observation a2e974f8-91ff-42a0-9d7e-0b98c57259ee · outbound

This paper cites Kawasaki, Y.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Kawasaki, Y

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.716957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.571367Z digest=sha256:ed87f2445fe2b05b3b9a1a157cf67983be5a500fc503caf4bdff80115a877f12

Observation a06c340d-260f-4a4d-938e-bf5017dc5768 · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.699627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.575789Z digest=sha256:69e6adb01f92718a70700fca56ae0ae2d44899b1bfd52f40c5298d3a6547e2d7

Observation 72a08216-8dfc-4a7c-be6b-73fbe6ddeb0b · outbound

This paper cites an unresolved cited work.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Unresolved cited work

Reference 50

Resolution
unresolved
raw_fallback, observed 2026-08-14T05:17:29.682881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.581047Z digest=sha256:b042b8762dda6f14a2810dfd002348bd461cb345cb6907d05bc9c1f65dc594b1

Observation d7f2dc7c-d5df-4c4e-ac3e-56f46cac8b54 · outbound

This paper cites Carpena, P.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Carpena, P

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.668009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.585823Z digest=sha256:d56de15a6165a07156890fffbe288c1c47ac56054569484d623a0597bfa0c23a

Observation aed4caa7-23a2-406b-933c-0afdff7a018c · outbound

This paper cites Carpena, J.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Carpena, J

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.652377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.590684Z digest=sha256:f83bdca60653fdbf45f548e7b84622d5ebeda005deb20411b8f97456776a54b4

Observation a57e335f-15b7-4ff6-b474-77781c8ad7d4 · outbound

This paper cites Podobnik, D.F.

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Podobnik, D.F

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T05:17:29.635358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-14T05:17:29.596158Z digest=sha256:7bf66a987b764713e9e9c003e250ac03d69357e33814af059e0cce61bf3291f6

Pith citing papers

No inbound Pith citation observations are available.