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Record W2971398685 · doi:10.24123/jsh.v8i1.672

THE LOW-COST CLAYMATION DEVELOPMENT WITH A CASE STUDY OF “ABDI DALEM” COMIC CHARACTER ADAPTATION

2014· article· en· W2971398685 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

VenueSosial & Humaniora · 2014
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology in Education and Learning
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsAnimationComicsCharacter (mathematics)Adaptation (eye)Computer scienceProduction (economics)Process (computing)Order (exchange)Motion (physics)AnimeMultimediaAdvertisingBusinessArtificial intelligencePsychologyComputer graphics (images)EconomicsMathematics

Abstract

fetched live from OpenAlex

Indonesia is one of the most largest country that is growing and has the potential of creative industries from various fields, one of which is animation and games. The majority of human resources of Indonesia are now advancing animation and games, but is still hampered by the right technology. Stop motion as an animation technique has a difficult position as mastering, this type of animation required technology and craftsmanship, thus it does not attract many people to use this technique. This research will discuss the development of a lowcost stop motion animation with maximum result that aims to support the needs of creative industry in Indonesia. This study is taking a character from ‘Abdi Dalem’; a popular fictional comic series. In Order to reach lower production costs, This research is using comparative study method in regards of low-cost materials and experimental method to determine the most economical post-production process.

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.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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