Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:30:36.424920Z
Paper Citation Record · LEDGER
As of 17 August 2026, this Paper Citation Record lists 100 of 111 outbound references and 0 inbound Pith citation observations for arXiv:2505.04681.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:30:36.424920Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 111 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 98b8531a-939a-4a88-8b8f-5e6ef37e2984 · outbound
A data-driven approach for star formation parameterization using symbolic regression , " * write output.state after.block = add.period write newline
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A data-driven approach for star formation parameterization using symbolic regression write newline
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A data-driven approach for star formation parameterization using symbolic regression w'w8 j- /|bI<+ X + lp y pKcZe=xa+\^ t xXw^ 狏,X/yrU˒`6 PWIpr.yqc,](r l^D Uyi;L 93NXq+NmdDI
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A data-driven approach for star formation parameterization using symbolic regression N., Lisenfeld , U., et al
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A data-driven approach for star formation parameterization using symbolic regression Unresolved cited work
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A data-driven approach for star formation parameterization using symbolic regression Unresolved cited work
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A data-driven approach for star formation parameterization using symbolic regression Unresolved cited work
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Observation eccdb2a9-2ac1-4762-9b9c-ca99b6290705 · outbound
A data-driven approach for star formation parameterization using symbolic regression 2022, title From EMBER to FIRE: predicting high resolution baryon fields from dark matter simulations with deep learning , , 509, 1323, 10.1093/mnras/stab3088
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Observation b1c061c9-ade8-4fc8-bda2-529195472f68 · outbound
A data-driven approach for star formation parameterization using symbolic regression 2008, title The Star Formation Law in Nearby Galaxies on Sub-Kpc Scales , , 136, 2846, 10.1088/0004-6256/136/6/2846
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Observation 216bb681-e0fd-4ebc-915d-2a3d9c0cb61c · outbound
A data-driven approach for star formation parameterization using symbolic regression D., Leroy , A
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Observation 06eaca8b-c6db-44ce-9801-2a4c6c18e74a · outbound
A data-driven approach for star formation parameterization using symbolic regression 2018, title The Star Formation Rate in the Gravoturbulent Interstellar Medium , , 863, 118, 10.3847/1538-4357/aad002
Reference 11
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Observation 9998b60e-b1cd-469b-96e3-f7faa3f8d8d3 · outbound
A data-driven approach for star formation parameterization using symbolic regression 2003, title Galactic Stellar and Substellar Initial Mass Function , , 115, 763, 10.1086/376392
Reference 12
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Observation 92b2ee4c-444d-41c1-9df1-b10f66323669 · outbound
A data-driven approach for star formation parameterization using symbolic regression XGBoost: A Scalable Tree Boosting System
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Observation 945089fb-2d13-4757-9f69-5c602fe8bea5 · outbound
A data-driven approach for star formation parameterization using symbolic regression A survey and taxonomy of loss functions in machine learning
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A data-driven approach for star formation parameterization using symbolic regression G., Baugh , C
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Observation e2c41148-cd5f-46ee-ba13-27bea14b1dbd · outbound
A data-driven approach for star formation parameterization using symbolic regression E., Schinnerer , E., et al
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Observation f19e6fd0-251a-4eac-b81c-251f4326dc4b · outbound
A data-driven approach for star formation parameterization using symbolic regression Vertical equilibrium of molecular gas in galaxies
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A data-driven approach for star formation parameterization using symbolic regression A., Schaye , J., Bower , R
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A data-driven approach for star formation parameterization using symbolic regression Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
Reference 19
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Observation 0fb6891c-9b06-4ab1-893d-46da61ad8d22 · outbound
A data-driven approach for star formation parameterization using symbolic regression 1975, title I -Divergence Geometry of Probability Distributions and Minimization Problems , The Annals of Probability, 3, 146 , 10.1214/aop/1176996454
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A data-driven approach for star formation parameterization using symbolic regression 2010 a , title Very High Gas Fractions and Extended Gas Reservoirs in z = 1.5 Disk Galaxies , , 713, 686, 10.1088/0004-637X/713/1/686
Reference 21
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Observation c7af4cf9-17cc-4399-94c2-4f9e5d9a2b82 · outbound
A data-driven approach for star formation parameterization using symbolic regression 2010 b , title Different Star Formation Laws for Disks Versus Starbursts at Low and High Redshifts , , 714, L118, 10.1088/2041-8205/714/1/L118
Reference 22
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Observation b6de39f3-9f88-4d59-9a80-bc3ae75307aa · outbound
A data-driven approach for star formation parameterization using symbolic regression L., & Jin , Z
Reference 23
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A data-driven approach for star formation parameterization using symbolic regression L., Pan , H.-A., Bluck , A
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Observation b7e62518-a9d1-44e3-841d-46a614eb6650 · outbound
A data-driven approach for star formation parameterization using symbolic regression G., & Scalo , J
Reference 25
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Observation a2e8e729-4939-4dfb-a95e-98aa1afa1e56 · outbound
A data-driven approach for star formation parameterization using symbolic regression 1992, title The small-scale density and velocity structure of quiescent molecular clouds , , 257, 715
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Observation f71c7e71-0b7c-452c-b9ad-6ada870d0bed · outbound
A data-driven approach for star formation parameterization using symbolic regression Unresolved cited work
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Observation a75c6bd6-6725-4d9f-94f1-af1669726e00 · outbound
A data-driven approach for star formation parameterization using symbolic regression 2010, PhD thesis, University of Heidelberg, Heidelberg
Reference 28
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Observation 7745eb78-93b3-4592-92cc-816411400f29 · outbound
A data-driven approach for star formation parameterization using symbolic regression 2013, title The origin of physical variations in the star formation law , , 436, 3167, 10.1093/mnras/stt1799
Reference 29
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Observation d95e1680-fe1c-4ac8-876e-aa94c28daf2d · outbound
A data-driven approach for star formation parameterization using symbolic regression 2016, title On the universality of interstellar filaments: theory meets simulations and observations , , 457, 375, 10.1093/mnras/stv2880
Reference 30
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A data-driven approach for star formation parameterization using symbolic regression Unresolved cited work
Reference 31
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A data-driven approach for star formation parameterization using symbolic regression S., & Schmidt , W
Reference 32
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A data-driven approach for star formation parameterization using symbolic regression M., Longmore , S
Reference 33
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Observation 3d805331-da52-4816-af4b-3ad7b3655a19 · outbound
A data-driven approach for star formation parameterization using symbolic regression 2023, title FIREbox: simulating galaxies at high dynamic range in a cosmological volume , , 522, 3831, 10.1093/mnras/stad1205
Reference 34
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A data-driven approach for star formation parameterization using symbolic regression A., Pipher , J
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A data-driven approach for star formation parameterization using symbolic regression J., Allen , L
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Observation 0b9dd599-9ae2-4d60-8977-b57e449b3a73 · outbound
A data-driven approach for star formation parameterization using symbolic regression 2011, title Analytical Star Formation Rate from Gravoturbulent Fragmentation , , 743, L29, 10.1088/2041-8205/743/2/L29
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Observation 431c4dce-2bfd-4baf-9327-fb434c8f7977 · outbound
A data-driven approach for star formation parameterization using symbolic regression 2013, title Analytical Theory for the Initial Mass Function
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A data-driven approach for star formation parameterization using symbolic regression H., & Brunt , C
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A data-driven approach for star formation parameterization using symbolic regression F., Quataert , E., & Murray , N
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A data-driven approach for star formation parameterization using symbolic regression F., Wetzel , A., Kere s , D., et al
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A data-driven approach for star formation parameterization using symbolic regression E., Schinnerer , E., et al
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A data-driven approach for star formation parameterization using symbolic regression C., & Evans , N
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A data-driven approach for star formation parameterization using symbolic regression 2001, title On the variation of the initial mass function , , 322, 231, 10.1046/j.1365-8711.2001.04022.x
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A data-driven approach for star formation parameterization using symbolic regression R., Burkhart , B., Forbes , J
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A data-driven approach for star formation parameterization using symbolic regression R., Dekel , A., & McKee , C
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A data-driven approach for star formation parameterization using symbolic regression R., & McKee , C
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A data-driven approach for star formation parameterization using symbolic regression R., McKee , C
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A data-driven approach for star formation parameterization using symbolic regression J., Lombardi , M., & Alves , J
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A data-driven approach for star formation parameterization using symbolic regression D., et al
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A data-driven approach for star formation parameterization using symbolic regression 2011, title Star Formation Efficiencies and Lifetimes of Giant Molecular Clouds in the Milky Way , , 729, 133, 10.1088/0004-637X/729/2/133
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A data-driven approach for star formation parameterization using symbolic regression 2002, title The Stellar Initial Mass Function from Turbulent Fragmentation , , 576, 870, 10.1086/341790
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A data-driven approach for star formation parameterization using symbolic regression 2018, title Simulating galaxy formation with the IllustrisTNG model , , 473, 4077, 10.1093/mnras/stx2656
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A data-driven approach for star formation parameterization using symbolic regression 2012, title Star Formation Laws and Thresholds from Interstellar Medium Structure and Turbulence , , 760, L16, 10.1088/2041-8205/760/1/L16
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A data-driven approach for star formation parameterization using symbolic regression 2012, title Cluster-formation in the Rosette molecular cloud at the junctions of filaments , , 540, L11, 10.1051/0004-6361/201118566
Reference 92
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A data-driven approach for star formation parameterization using symbolic regression K., Walter , F., et al
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A data-driven approach for star formation parameterization using symbolic regression Unresolved cited work
Reference 94
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A data-driven approach for star formation parameterization using symbolic regression M., & Dye , S
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A data-driven approach for star formation parameterization using symbolic regression S., & Dav \'e , R
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A data-driven approach for star formation parameterization using symbolic regression C., Springel , V., et al
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A data-driven approach for star formation parameterization using symbolic regression K., Ostriker , E
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A data-driven approach for star formation parameterization using symbolic regression K., Rosolowsky , E., et al
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