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Soft Union Tri-bi-ideals of Semigroups

2025· article· W4417272095 on OpenAlexvenueno aff
Aleyna İlgin, Aslıhan Sezgin, Thiti Gaketem

Bibliographic record

VenueInternational Journal of Analysis and Applications · 2025
Typearticle
Language
FieldDecision Sciences
TopicFuzzy and Soft Set Theory
Canadian institutionsnot available
FundersUniversity of Phayao
KeywordsConverseSemigroupIdeal (ethics)GeneralizationCommutative propertySoft setOrder (exchange)Set (abstract data type)

Abstract

fetched live from OpenAlex

The concept of tri-quasi ideal was presented as a generalization of quasi-ideal, interior ideal, and bi-ideal. In this paper, we transfer this concept to soft set theory and semigroups,and introduce a novel type of soft union (S-uni) ideal form called "soft union (S-uni) tri-bi-ideal”. The main aim of this study is to obtain the relations between S-uni tri-bi-ideals and other certain types of S-uni ideals of a semigroup. Our results show that every S-uni tri-bi-ideal of a band is an S-uni subsemigroup. Moreover, an S-uni tri-bi-ideal is a generalization of an S-uni ideal, interior ideal, bi-ideal, quasi-ideal, weak-interior ideal, bi-interior ideal and bi-quasi ideal, however in order to satisfy the converses, the semigroup should have specific conditions. We also demonstrate that the S-uni quasi-interior ideal of a left or right simple semigroup is an S-uni tri-bi-ideal, nevertheless the converse holds for the zero semigroup. Furthermore, the S-uni bi-quasi-interior ideal of a commutative semigroup is an S-uni tri-bi-ideal, however, for the converse to hold the semigroup must be a band. We have shown that an S-uni tri-ideal coincides with an S-uni tri-bi-ideal of a band, and every S-uni tri-bi-ideal of a group is an S-uni tri-ideal. We also obtain a relation between tri-bi-ideal and its soft characteristic function, enabling us to get the relation between semigroup and soft set theory. Furthermore, we present conceptual characterizations and analysis of the new concept in terms of the soft set operations, the soft (anti/inverse) image, supporting our assertions with illuminating examples.

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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0020.005
Open science0.0000.003
Research integrity0.0000.002
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 head0.021
GPT teacher head0.375
Teacher spread0.354 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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