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Record W4413965965 · doi:10.70930/tac/rkp3zgxc

Tight spans, Isbell completions and semi-tropical modules

2013· article· en· W4413965965 on OpenAlexvenueno aff
Simon Willerton

Bibliographic record

VenueTheory and applications of categories · 2013
Typearticle
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

In this paper we consider generalized metric spaces in the sense of Lawvere and the categorical Isbell completion construction.We show that this is an analogue of the tight span construction of classical metric spaces, and that the Isbell completion coincides with the directed tight span of Hirai and Koichi.The notions of categorical completion and cocompletion are related to the existence of semi-tropical module structure, and it is shown that the Isbell completion (hence the directed tight span) has two different semi-tropical module structures. Introduction.This paper grew out of a desire to understand whether the tight span of a metric space could be understood in terms of the enriched category theory approach to metric spaces.This led to understanding a link between two apparently unrelated constructions of Isbell, namely the tight span of metric spaces and the Isbell completion of categories; this is turn led, via categorical completeness, to connections with tropical algebra.It seems interesting that these two constructions of Isbell remained unconnected for nearly fifty years.In this introduction the main ideas of Isbell completion, semi-tropical algebra and tight spans will be given.The intention is that this paper should be readable by mathematicians interested in metric spaces or tropical algebra, without much category theory background, and to allow them to see how category theoretic methods give interesting insight in this case.This means that some bits of enriched category theory for metric spaces will be spelt out in some detail.The Isbell completion of a generalized metric space.Lawvere [18] observed that a metric space can be viewed as something similar to a category and that from that perspective there is a natural generalization -generalized metric space -which means a set X with a 'distance' function d : X X [0, ] such that d(x, x) = 0 and d(x, y) + d(y, z) d(x, z) for all x, y, z X, with no further conditions like symmetry imposed.Generalized metric spaces can be thought of as directed metric spaces.From a category theoretic point of view, generalized metric spaces are precisely [0, ]-enriched categories and so much of the machinery of category theory can be utilized to study them.In this paper we will look at the 'Isbell completion' for generalized metric spaces.

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How this classification was reachedexpand

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 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: none
Teacher disagreement score0.983
Threshold uncertainty score0.257

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)0.0000.000

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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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 teacher head, 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".

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Citations3
Published2013
Admission routes1
Has abstractyes

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