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Record W2945710931

How high does Paper Mario have to jump to match the strength of his regular counterpart

2019· article· en· W2945710931 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Interdisciplinary Science Topics · 2019
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSuperstarJumpLong jumpJumpingOrder (exchange)MathematicsComputer scienceMathematical economicsSimulationEconomicsPhysicsArtArt historyGeology
DOInot available

Abstract

fetched live from OpenAlex

The video game superstar Mario is well known for his jumping ability. In the spin-off game, Paper Mario is similarly well-known but is physically made of paper. This paper explores the differences in the impact force between regular and Paper Mario and calculates the jump height Paper Mario would need to attain in order for him to carry the same impact force as regular Mario. To do this, Paper Mario is assumed to be a rectangular sheet of paper, and the same height as regular Mario, but much less dense. From calculating the impact force from regular Mario to be 17.3 kN, it was found that in order to match this force, Paper Mario would need to attain a height of 47.6 m. As a result, while it is possible for Paper Mario to match Mario in damage, it is unrealistic that he would be able to do so. He can however, jump multiple times on enemies which would increase his damage output.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0040.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.284
Teacher spread0.272 · 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