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Record W2074267772 · doi:10.2495/dne-v5-n3-254-267

Few large and many small: Hierarchy in movement on earth

2010· article· en· W2074267772 on OpenAlexvenueno aff
Sylvie Lorente, Adrian Bejan

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Thermodynamics and Statistical Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsConstructal lawMovement (music)EnergeticsEffiScale (ratio)HierarchyKilogramEnvironmental scienceGeographyComputer sciencePhysicsMechanicsBody weight

Abstract

fetched live from OpenAlex

movement on earth is effected by bodies (e.g. river channels, animals, vehicles) of seemingly random scales, large and small. here we use the constructal law and the design of 'distributed energy systems' to show that the large must be few and the small many, in particular proportions that are required for greater access for movement on areas. First, we demonstrate that mass movement per kilogram moved and kilometer traveled requires less fuel on larger vehicles. The thermodynamics basis of this is the same as for the effect of size on the efficiency of motors, vascular flow architectures and river basins. The same principle dictates that on every vehicle the motor mass must scale with the structural mass, and with the total mass. In addition, larger masses must move on areas to greater distances, and a characteristic number of smaller masses must be allocated to a larger mass to travel, on the same area element.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.006
GPT teacher head0.251
Teacher spread0.245 · 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 designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations27
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and EcodynamicsSame topicAdvanced Thermodynamics and Statistical MechanicsFrench-language works237,207