{"id":"W2102210226","doi":"10.1109/imtc.1994.351900","title":"Visual measurement of orientation using ceiling features","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Artificial intelligence; Computer vision; Ceiling (cloud); Computer science; Orientation (vector space); Feature extraction; Binary number; Line (geometry); Feature (linguistics); Image processing; Sampling (signal processing); Pattern recognition (psychology); Image (mathematics); Mathematics; 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":[0.0002562352,0.0005161471,0.0004911272,0.001194131,0.0002718805,0.0008064144,0.0004908827,0.0003685036,0.005971714],"category_scores_gemma":[0.001095858,0.0003517244,0.0002793974,0.0007010257,0.0003157303,0.0006238261,0.0007796732,0.0006373079,0.001711003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002796414,"about_ca_system_score_gemma":0.0004169213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001954956,"about_ca_topic_score_gemma":0.002298992,"domain_scores_codex":[0.999655,0.00003187936,0.00001353647,0.00009700997,0.0001550605,0.00004750736],"domain_scores_gemma":[0.9994018,0.00009646578,0.00008205845,0.0001297005,0.0002162581,0.00007371993],"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":[0.000325447,0.00006279101,0.004392142,0.0002626125,0.00002838208,0.0001737847,0.0001422426,0.002657862,0.7237493,0.001592081,0.002157244,0.2644561],"study_design_scores_gemma":[0.0001179978,0.001205702,0.1171864,0.0002161453,0.0001400277,0.003701864,0.0003013063,0.1088061,0.7340817,0.003232102,0.03076562,0.0002450976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1731355,0.0004980991,0.8055145,0.00009075247,0.0001111807,0.0002268482,0.001126645,0.008579458,0.01071698],"genre_scores_gemma":[0.5615325,0.0004380032,0.4318596,0.00007952523,0.00005265112,0.0001788746,0.001228613,0.0005817755,0.004048456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005971714,"threshold_uncertainty_score":0.01997733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05959841648528715,"score_gpt":0.3239696988384194,"score_spread":0.2643712823531323,"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."}}