{"id":"W1501872100","doi":"10.1109/ias.1990.152385","title":"Array-code identification tags","year":2002,"lang":"en","type":"article","venue":"","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Code (set theory); Identification (biology); Computer science; Source code; Programming language","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":[0.001045502,0.0011011,0.0008973125,0.002430047,0.001160862,0.002810152,0.002054182,0.001871104,0.09378996],"category_scores_gemma":[0.006259426,0.0007121022,0.0004049357,0.002303482,0.000642416,0.00331927,0.001808688,0.001275218,0.07799365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007937241,"about_ca_system_score_gemma":0.00098857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006997291,"about_ca_topic_score_gemma":0.0009836096,"domain_scores_codex":[0.9981018,0.0002074677,0.0001226434,0.0002965399,0.001105414,0.0001661441],"domain_scores_gemma":[0.9941627,0.001029707,0.000325639,0.001453891,0.002766413,0.0002615453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007267012,0.0001178002,0.00185548,0.0009342968,0.00003411466,0.0007755106,0.0005934659,0.003868276,0.1457146,0.1098012,0.3100585,0.4255201],"study_design_scores_gemma":[0.00004828689,0.0001690723,0.0007606537,0.0001295109,0.00002947972,0.001707944,0.0001582267,0.01244177,0.1527476,0.01182852,0.8198352,0.0001436923],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007986391,0.001271031,0.8488524,0.0006139757,0.001906241,0.0006439699,0.00636829,0.03536451,0.09699325],"genre_scores_gemma":[0.1019899,0.001634903,0.6128925,0.002388543,0.0007088151,0.001153302,0.01595987,0.007758319,0.2555139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09378996,"threshold_uncertainty_score":0.3137587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907576762723245,"score_gpt":0.2361012611054833,"score_spread":0.2070254934782508,"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."}}