{"id":"W4200481813","doi":"10.21203/rs.3.rs-1069411/v1","title":"A Marker-Less Monocular Vision Point Positioning Method for Industrial Manual Operation Environments","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; University of Alberta","keywords":"Computer vision; Monocular vision; Artificial intelligence; Point (geometry); Monocular; Computer science; Mathematics; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003149405,0.0006146866,0.0007333453,0.0006537084,0.0003822755,0.0005838734,0.00119714,0.0006563006,0.004262896],"category_scores_gemma":[0.0009112192,0.0005172846,0.0003354395,0.0008509074,0.0002233725,0.0006536112,0.0009257839,0.000617667,0.002194562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002415104,"about_ca_system_score_gemma":0.0007145801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002874125,"about_ca_topic_score_gemma":0.004113444,"domain_scores_codex":[0.9992653,0.0000889925,0.00002485843,0.000164945,0.0004084366,0.00004745425],"domain_scores_gemma":[0.999527,0.0000699005,0.00004123146,0.0001248416,0.0002086012,0.0000284235],"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":[0.0003054858,0.00008353135,0.0003905215,0.0001732375,0.00003443678,0.0000702119,0.0001275993,0.00734634,0.1742042,0.002016454,0.003397425,0.8118505],"study_design_scores_gemma":[0.0001287554,0.0007334062,0.01010555,0.00007175927,0.0001195539,0.001697478,0.0001540315,0.7357641,0.2139667,0.002880609,0.03420731,0.0001707542],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008496592,0.0002015689,0.9889562,0.00002606674,0.00006593454,0.00002559242,0.0000661384,0.001220503,0.0009414001],"genre_scores_gemma":[0.1962784,0.0003691524,0.7936985,0.0000751495,0.00005500242,0.0000937924,0.0003545658,0.0001954827,0.008880044],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004262896,"threshold_uncertainty_score":0.01426083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1113046577471548,"score_gpt":0.4467517756145454,"score_spread":0.3354471178673906,"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."}}