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Record W4200213419 · doi:10.1002/9781119761990.ch18

Terraforming Mars: A Cabinet of Curiosities

2021· other· en· W4200213419 on OpenAlexaff
Martin Beech

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of ReginaCampion College
Fundersnot available
KeywordsMars Exploration ProgramMartianAstrobiologySituatedMars landingAtmosphere of MarsExploration of MarsHistoryEnvironmental ethicsComputer scienceGeographyArtificial intelligencePhilosophyPhysics

Abstract

fetched live from OpenAlex

This review explores the background history and contemporary thinking concerning the terraforming of Mars. The end results of any such physical transformation have long been articulated, and they are to make Mars better suited to habitation by humans. It is still far from clear, however, how the required changes to Mars might be brought about. Indeed, a spectrum of terraforming options are available, and the foremost problem to be addressed in the current epoch is whether Mars should be terraformed at all, and if so for who and by whom. The essential aim of any Martian terraforming option must be to increase the atmospheric temperature, pressure, and composition, and this will require a massive, and sustained multi-generational effort. In terms of physical engineering, many terraforming options have been proposed, and in this review, we examine their origins, feasibility, and methodologies. It is concluded that Mars can, in principle, be transformed into a state better suited to support human habitation on a timescale situated between centuries to millennia.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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