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Record W3107248814 · doi:10.31274/rtd-20201118-272

Enriching representable functors

2002· dissertation· en· W3107248814 on OpenAlexaboutno aff
Stefan Rothbauer

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicConstraint Satisfaction and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsFunctorObject (grammar)MathematicsClass (philosophy)IdempotenceSet (abstract data type)Commutative propertyAlgebra over a fieldAlgebraic structureVector spaceAlgebraic numberCategory theoryRing (chemistry)Pure mathematicsComputer scienceProgramming languageArtificial intelligence

Abstract

fetched live from OpenAlex

As George Bergman pointed out at the International Conference of Mathematicians in Vancouver (1974) category theory can be a very efficient way to determine all possible operations on sets. In fact, under certain conditions the operations on the values of a (representable) set valued functor are in one to one correspondence with the cooperations on the representing object and the relations that hold between those operations can be determined by analyzing the relations that hold between the cooperations on the representing object. In this thesis the ideas of Bergman described above together with the approaches described below are applied to the group of units of an arbitrary ring and the idempotent elements of commutative rings. Algebra, one of the oldest and still most popular branches of mathematics, is the study of algebraic structures. An algebraic structure, loosely speaking, is a object together with one or more operations on it. If there is more than one operation on the set these operations often satisfy certain relations. The class of objects which share certain properties is called a category. Some of the most common such categories consist of objects which posses an underlying set (e.g. groups, rings, fields, vector spaces). Often these sets can be regarded as members of different categories and accordingly are endowed with a number of operations between which relations hold. In the case that a particular object is part of different categories, one can try (more often than not successfully) to make use of certain facts that are true in one category to gain knowledge about other objects in the second category. One other approach to analyze algebraic structures is to look at their substructures (whether those lie within this category or in a different one) and gain knowledge about the original object by composing the pieces of the puzzle gained by analyzing the sub objects. Frequently the opposite approach works quite as well, i.e. to look at the objects which contain an object as subobjects.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.802
Threshold uncertainty score0.999

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)0.0020.000

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 head0.236
Teacher spread0.224 · 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.

Study designOther design
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

Citations0
Published2002
Admission routes1
Has abstractyes

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