{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004807171,0.00008259452,0.00008611607,0.00002451213,0.00003830839,0.00003236884,0.00003963932,0.00008356359,0.00003547846],"category_scores_gemma":[0.000007221811,0.00007164709,0.00002236988,0.0000472583,0.000003259101,0.0001532522,0.000007472079,0.0001683284,0.00002172389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002707865,"about_ca_system_score_gemma":0.000003735877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003516077,"about_ca_topic_score_gemma":0.00001637453,"domain_scores_codex":[0.9996551,0.000003220962,0.00007712916,0.00007811868,0.00005136241,0.0001350969],"domain_scores_gemma":[0.9998424,0.000006945386,0.0000138094,0.00007711564,0.00002879031,0.00003090562],"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":[0.000001702489,0.00000128686,6.38435e-7,0.00007156773,0.000004155552,0.000003518379,0.00001594627,0.0002868372,0.9891911,0.000899695,0.00004439503,0.009479179],"study_design_scores_gemma":[0.0001638098,0.000005115713,0.0000135172,0.00006282118,0.00001411889,0.00009590517,0.0001193537,0.07550044,0.9210639,0.0003295335,0.002428747,0.0002027496],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701478,0.00005656065,0.0210117,0.00000595987,0.005782211,0.00009210258,0.00000728408,0.0006949838,0.002201383],"genre_scores_gemma":[0.9690449,0.000004365716,0.03050327,0.00001083354,0.000320195,0.000002506849,0.000009486213,0.00002444096,0.00007999683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0752136,"threshold_uncertainty_score":0.2921683,"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."}}