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Record W4309000537 · doi:10.1007/978-3-031-13264-3_11

Convention on International Liability for Damage Caused by Space Objects

2022· book-chapter· en· W4309000537 on OpenAlexaboutno aff
Kirsten Schmalenbach

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsnot available
Fundersnot available
KeywordsLiabilityHuman spaceflightOuter spaceHazardous wasteSpace (punctuation)AeronauticsNuclear weaponNuclear powerConventionSpace debrisAccident (philosophy)LawSpace lawEngineeringPolitical scienceForensic engineeringAerospace engineeringPhysicsSpacecraftComputer scienceWaste management

Abstract

fetched live from OpenAlex

Abstract For obvious reasons, space activities are generally classed as ultra-hazardous endeavours, i.e. they are inherently dangerous, not only for the various vehicles leaving the Earth atmosphere, but also the cargo such vehicles carry, be it human or otherwise. Additionally, space activities generate environmental risks in outer space that can, at times, impact the Earth as what goes up, must inevitably come down. The hazards of spaceflight come from multiple sources, including, but not limited to, the technology used (e.g. nuclear power sources) and the hostile nature of outer space which is difficult to reach, difficult to survive in and difficult to return from. This inherent danger has been tragically highlighted not only by the shuttle disasters involving Challenger (1986) and Columbia (2003) but also by the vast field of radioactive debris left in Canada after the uncontrolled re-entry of the Soviet satellite Cosmos 954 in 1977. The latter accident exemplifies that ultra-hazardous activities require liability rules because when something goes wrong, the consequences can be significant for any injured parties.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.004
Scholarly communication0.0100.004
Open science0.0040.003
Research integrity0.0220.018
Insufficient payload (model declined to judge)0.0200.019

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.256
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2022
Admission routes1
Has abstractyes

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