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The Lumen Orbit white paper (September 2024) names on-orbit safety and orbital-debris mitigation as a key hurdle for orbital data centres, and says that their larger physical size makes them answerable for keeping the probability of collisions low. Copy claim link
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Machine-checked Stated in source Supported
Supporting evidence: primary · 1 source
Date 2024-09 · publication Records dated 2024 → 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 Earliest located Register (kind of text) Nonfiction Scope and limits States the debris and collision part of the proposition only; on astronomy the same paper argues that the night sky is unlikely to be affected, which its contesting record keeps. A proponent’s text that names the risk and argues it can be handled. Recorded by the maintainer’s decision of 2026-11-07 (re-check sheet, decision 6c) after the cross-family re-check of 2026-11-02 (role 165). Earliest located means the earliest text found by the collection’s searches and that re-check, not a first statement.
The evidence 02 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 “one key hurdle is on-orbit safety and orbital debris mitigation concerns, including decommissioning” Location in source PDF p. 5, "Speed of Deployment"
How it was read Retrieved directly from the publisher
Machine-checked · 2026-11-07
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. On 2026-11-04 a model of another family read the cover date, September 2024, in the linked capture of 24 December 2024, whose SHA-256 matches the stored value (research/archive-batch-2026-11-04/codex-report.md, D16 reads). No earlier day of publication is inferred. 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 The quotation (13 words) matched on 2026-11-07 the white paper as retrieved that day, whose SHA-256 (90e1bde5b01437d7778e044f8ed95df1c49a63559fc1e35177c94d6443e384f7) equals the stored value. Matching ignored differences of whitespace and quotation marks. No human editorial review. The same passage is quoted for the paper’s contesting record. 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 “demonstrate the probability of collisions with other objects is sufficiently low. Given their larger physical size, orbital data centers must be especially responsible users of low Earth orbit” Location in source PDF p. 5, "Speed of Deployment"
How it was read Retrieved directly from the publisher
Machine-checked · 2026-11-07
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. On 2026-11-04 a model of another family read the cover date, September 2024, in the linked capture of 24 December 2024, whose SHA-256 matches the stored value (research/archive-batch-2026-11-04/codex-report.md, D16 reads). No earlier day of publication is inferred. 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 The quotation (28 words) matched on 2026-11-07 the white paper as retrieved that day, whose SHA-256 (90e1bde5b01437d7778e044f8ed95df1c49a63559fc1e35177c94d6443e384f7) equals the stored value. Matching ignored differences of whitespace and quotation marks. No human editorial review. Status and meaning Editorial status. Machine-checked . Machines found this record and checked it against its sources; no person accepted it individually. Each evidence record above shows how it was checked.
Relation. A passage of the document meets the proposition’s criterion. The record documents what the text states, in its register (fiction and satire are labelled); it is not evidence that the proposition is true and implies no influence on or from another text.
Date. Date of publication, as the source gives it.
Record ID cpo-orb-oc11-lumen-2024-states