{"id":"W4248973820","doi":"10.1109/ismar.2010.5643616","title":"Origami recognition system using natural feature tracking","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Materials and Mechanics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Feature (linguistics); Tracking (education); Folding (DSP implementation); Natural (archaeology); Artificial intelligence; Computer vision; Tracking system; Feature extraction; Human–computer interaction; Computer graphics (images); Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002757782,0.0002565254,0.0003598824,0.000272992,0.0003064186,0.0005766126,0.0006840383,0.0007147817,0.003401111],"category_scores_gemma":[0.0004901803,0.0002180888,0.0002729061,0.0001710462,0.0001776775,0.001081378,0.0004903932,0.0004779379,0.001972935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000272587,"about_ca_system_score_gemma":0.0002884084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009588512,"about_ca_topic_score_gemma":0.001211834,"domain_scores_codex":[0.9997621,0.00001633967,0.00001594252,0.00007603655,0.00010715,0.00002237345],"domain_scores_gemma":[0.9998541,0.00003025577,0.00001646569,0.00003720969,0.00004698106,0.00001496627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002154691,0.0001159019,0.0007858731,0.00008830682,0.00002525147,0.0003132127,0.0001181719,0.002235785,0.6872485,0.003500637,0.004007503,0.3013453],"study_design_scores_gemma":[0.00005467617,0.0004194733,0.004420805,0.00002952505,0.00005206565,0.001403848,0.00004826936,0.2531185,0.7023862,0.002347958,0.03562565,0.00009307428],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07513539,0.0006027226,0.905731,0.0002718249,0.0003580693,0.0001707272,0.0002091782,0.008745884,0.008775171],"genre_scores_gemma":[0.381943,0.0004436905,0.5971918,0.0003919537,0.0000653612,0.0001845848,0.0006241073,0.00020288,0.01895258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003401111,"threshold_uncertainty_score":0.01137781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01254269787292612,"score_gpt":0.2166789278726769,"score_spread":0.2041362299997508,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}