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Record W4403080156 · doi:10.5391/ijfis.2024.24.3.194

Features Exploitation of YOLOv5-Based Freeze Backbone for Performance Improvement of UAV Object Detection

2024· article· en· W4403080156 on OpenAlexaff
Nur Qomariyati Laily, Jannah Nurul, Adhi Wibowo Suryo, Suprapto Siadari Thomhert

Bibliographic record

VenueInternational Journal of Fuzzy Logic and Intelligent Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Algorithms and Applications
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersUniversitas Telkom
KeywordsObject detectionArtificial intelligenceComputer scienceObject (grammar)Computer visionPattern recognition (psychology)

Abstract

fetched live from OpenAlex

Laily Nur Qomariyati, Nurul Jannah, Suryo Adhi Wibowo, and Thomhert Suprapto Siadari. International Journal of Fuzzy Logic and Intelligent Systems 2024;24:194-202. https://doi.org/10.5391/IJFIS.2024.24.3.194

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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