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Record W3103774624

When is a quantity additive, and when is it extensive? ∗

2008· article· en· W3103774624 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

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStatistical Mechanics and Entropy
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdditive functionSubadditivityNegationMathematicsStatistical physicsComputer sciencePhysicsDiscrete mathematicsMathematical analysis
DOInot available

Abstract

fetched live from OpenAlex

The difference between the terms additivity and extensivity, as well as their respective negations, is critically analyzed and illustrated with a few examples. The concepts of subadditivity, pseudo-additivity, and pseudo-extensivity are also defined. To say that a given quantity (physical or mathematical) is additive and to say that a quantity is extensive are two different affirmations which, unfortunately, appear too often as undifferentiated in the physics literature. The assimilation of the two terms is especially present in studies related to the entropy measure of Tsallis [1], and to the so-called field of non-extensive thermostatistics or Tsallis ’ statistics which is based on this measure of entropy [2]. In these studies, it is not uncommon to see the words additivity and extensivity being used as synonymous, and to read sentences such as “...the appropriate framework to describe non-extensive behavior is Tsallis ’ statistics, because of the non-additivity property of Tsallis ’ entropy. ” But, how exactly is the non-additivity property of Tsallis ’ entropy related to non-extensivity? Are these concepts linked together simply because they are thought to mean the same thing? These questions are raised not with the intention of criticizing the results related to nonextensive statistics; what is more important is the fact that they point to a somewhat misleading and careless usage of scientific jargon. That this carelessness persists would not by itself be so problematic, were it not for the fact that the difference between additivity and extensivity is at the very root of the issues raised by non-extensive statistics. For this reason, it seems more than advisable to rehabilitate the proper meaning of these two terms by reviewing their Contribution to the Proceedings of the International School and Conference on Non-Extensive Thermodynamics

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0260.000

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 head0.032
GPT teacher head0.265
Teacher spread0.233 · 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

Quick stats

Citations50
Published2008
Admission routes1
Has abstractyes

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