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The Lumen Orbit white paper (September 2024) states that a gigawatt-scale orbital data centre would need radiators dissipating gigawatts of heat, by far the largest deployed in space, while estimating them at under half the solar-array size. Copy claim link
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Machine-checked, not reviewed by an editor Stated in source Supported
Supporting evidence: primary · 1 source
Date 2024-09 · publication Wording attested 2024-12-24. The cover prints September 2024 (v1.01). The earliest copy located is an Internet Archive capture of 24 December 2024, which the cross-family check found byte-identical to the copy retrieved for the pack and again at admission. Role Context Register (kind of text) Nonfiction Missing element a statement that heat rejection is a significant constraint: the paper presents the radiator requirement as manageable Scope and limits Context from a proponent: it states the radiator requirement but presents it as manageable, so it does not meet the criterion as a constraint.
The evidence 03 primary source Supports the claim
Why we should train AI in space (White Paper v1.01) ↗ Lumen Orbit, Inc. (now Starcloud), via lumenorbit.github.io · 2024-09
Archived copy, captured 2024-12-24: Why we should train AI in space (White Paper v1.01) ↗
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Source excerpt “the data center will require radiators capable of radiatively dissipating gigawatts of thermal load” Location in source PDF p. 8, "Thermal Management"
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors Ezra Feilden; Adi Oltean; Philip Johnston Version Retrieved for the candidate pack on 2026-10-28 (SHA-256 90e1bde5b01437d7778e044f8ed95df1c49a63559fc1e35177c94d6443e384f7). PDF v1.01 as served at lumenorbit.github.io/wp.pdf on 2026-10-28; cover line "White Paper v1.01, September 2024". Starcloud's current link (starcloud.com/wp) serves v1.03 (starcloudinc.github.io/wp.pdf), which adds a note that Lumen Orbit was renamed Starcloud in 2025; v1.01 is quoted here. The served file cannot authenticate when the v1.01 bytes were first published. Retrieved again at admission on 2026-10-30 (SHA-256 90e1bde5b01437d7778e044f8ed95df1c49a63559fc1e35177c94d6443e384f7, the same bytes). The cross-family check (2026-10-30) opened the Internet Archive capture linked here and found it byte-identical to this retrieval; a capture shows only what the archive held on its date. Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Archive copy Open archived copy, captured 2024-12-24: Why we should train AI in space (White Paper v1.01) ↗ . An archive copy shows only what the archive retrieved on 2024-12-24. It does not prove what the page said earlier, and it can differ from the copy checked on 2026-10-28.Attribution Ezra Feilden, Adi Oltean, Philip Johnston, “Why we should train AI in space (White Paper v1.01)”, 2024-09. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, compute, power and orbit (research/compute-power-orbit). The quotation (14 words) matched the text retrieved for the candidate pack on 2026-10-28 and again at admission on 2026-10-30, in a fresh retrieval whose SHA-256 equals the pack’s. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (2026-10-29/30, item 93) re-read the record’s sources and found full support. No human editorial review. primary source Supports the claim
Why we should train AI in space (White Paper v1.01) ↗ Lumen Orbit, Inc. (now Starcloud), via lumenorbit.github.io · 2024-09
Archived copy, captured 2024-12-24: Why we should train AI in space (White Paper v1.01) ↗
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Source excerpt “by far the largest radiators deployed in space” Location in source PDF p. 8, "Thermal Management"
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors Ezra Feilden; Adi Oltean; Philip Johnston Version Retrieved for the candidate pack on 2026-10-28 (SHA-256 90e1bde5b01437d7778e044f8ed95df1c49a63559fc1e35177c94d6443e384f7). PDF v1.01 as served at lumenorbit.github.io/wp.pdf on 2026-10-28; cover line "White Paper v1.01, September 2024". Starcloud's current link (starcloud.com/wp) serves v1.03 (starcloudinc.github.io/wp.pdf), which adds a note that Lumen Orbit was renamed Starcloud in 2025; v1.01 is quoted here. The served file cannot authenticate when the v1.01 bytes were first published. Retrieved again at admission on 2026-10-30 (SHA-256 90e1bde5b01437d7778e044f8ed95df1c49a63559fc1e35177c94d6443e384f7, the same bytes). The cross-family check (2026-10-30) opened the Internet Archive capture linked here and found it byte-identical to this retrieval; a capture shows only what the archive held on its date. Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Archive copy Open archived copy, captured 2024-12-24: Why we should train AI in space (White Paper v1.01) ↗ . An archive copy shows only what the archive retrieved on 2024-12-24. It does not prove what the page said earlier, and it can differ from the copy checked on 2026-10-28.Attribution Ezra Feilden, Adi Oltean, Philip Johnston, “Why we should train AI in space (White Paper v1.01)”, 2024-09. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, compute, power and orbit (research/compute-power-orbit). The quotation (8 words) matched the text retrieved for the candidate pack on 2026-10-28 and again at admission on 2026-10-30, in a fresh retrieval whose SHA-256 equals the pack’s. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. A cross-family machine check (2026-10-29/30, item 93) re-read the record’s sources and found full support. No human editorial review. primary source Supports the claim
Why we should train AI in space (White Paper v1.01) ↗ Lumen Orbit, Inc. (now Starcloud), via lumenorbit.github.io · 2024-09
Archived copy, captured 2024-12-24: Why we should train AI in space (White Paper v1.01) ↗
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Cite the original source Cite the original document, not Selekoa. Check these details against it.
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Source excerpt “these radiators need to be less than half the size of the solar arrays” Location in source PDF p. 3, “Reduced Operating Expenses”
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors Ezra Feilden; Adi Oltean; Philip Johnston Version Retrieved for the candidate pack on 2026-10-28 (SHA-256 90e1bde5b01437d7778e044f8ed95df1c49a63559fc1e35177c94d6443e384f7). PDF v1.01 as served at lumenorbit.github.io/wp.pdf on 2026-10-28; cover line "White Paper v1.01, September 2024". Starcloud's current link (starcloud.com/wp) serves v1.03 (starcloudinc.github.io/wp.pdf), which adds a note that Lumen Orbit was renamed Starcloud in 2025; v1.01 is quoted here. The served file cannot authenticate when the v1.01 bytes were first published. Retrieved again at admission on 2026-10-30 (SHA-256 90e1bde5b01437d7778e044f8ed95df1c49a63559fc1e35177c94d6443e384f7, the same bytes). The cross-family check (2026-10-30) opened the Internet Archive capture linked here and found it byte-identical to this retrieval; a capture shows only what the archive held on its date. Canonical address Open the canonical source ↗ Accessed 2026-10-28 Language English (en) Archive copy Open archived copy, captured 2024-12-24: Why we should train AI in space (White Paper v1.01) ↗ . An archive copy shows only what the archive retrieved on 2024-12-24. It does not prove what the page said earlier, and it can differ from the copy checked on 2026-10-28.Attribution Ezra Feilden, Adi Oltean, Philip Johnston, “Why we should train AI in space (White Paper v1.01)”, 2024-09. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, compute, power and orbit (research/compute-power-orbit). The quotation (14 words) matched the text retrieved for the candidate pack on 2026-10-28 and again at admission on 2026-10-30, in a fresh retrieval whose SHA-256 equals the pack’s. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. Locator corrected at admission from PDF p. 8 (cross-family check, item 93). A cross-family machine check (2026-10-29/30, item 93) re-read the record’s sources and found full support. No human editorial review. Status and meaning Editorial status. Not reviewed by an editor . An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.
Relation. A passage of the document bears on the proposition without meeting its criterion; the record names the missing element. It is not an articulation and implies no influence.
Date. Date of publication, as the source gives it.
Record ID cpo-orb-oc6-lumen-2024