{"id":"W2106726701","doi":"10.1109/ccece.2008.4564808","title":"A simple visual compass with learned pixel weights","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer vision; Artificial intelligence; Pixel; Computer science; Weighting; Mobile robot; Compass; Rotation (mathematics); Robot; Image (mathematics); Simple (philosophy); Position (finance); Omnidirectional antenna; Geography; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002875561,0.0008980433,0.0007083053,0.0004260484,0.000345181,0.0006949913,0.001959559,0.00100996,0.00515232],"category_scores_gemma":[0.001195484,0.0004725757,0.0003486521,0.0006821524,0.0004769242,0.0008795753,0.0009424938,0.0006623192,0.002708467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003439131,"about_ca_system_score_gemma":0.0007783793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004523291,"about_ca_topic_score_gemma":0.006624245,"domain_scores_codex":[0.9995986,0.00003818725,0.00001491901,0.0001686844,0.0001369635,0.00004278392],"domain_scores_gemma":[0.9997476,0.00002525979,0.00002425222,0.0000750323,0.0001020399,0.00002592869],"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":[0.0003575532,0.000158203,0.00148067,0.0001157526,0.00009775697,0.00009084351,0.00005637063,0.1130838,0.05300477,0.006063154,0.00609445,0.8193968],"study_design_scores_gemma":[0.0001123951,0.0002489254,0.001895281,0.00002077682,0.00004490357,0.0002736431,0.00002532954,0.9604687,0.02406408,0.004940322,0.007852129,0.00005356662],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01475633,0.000137106,0.9802285,0.000119229,0.0001526972,0.000077013,0.00008663437,0.001755965,0.002686555],"genre_scores_gemma":[0.4321101,0.0002187394,0.5540912,0.0003073599,0.0001244582,0.0002235745,0.000290551,0.0001610702,0.01247291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00515232,"threshold_uncertainty_score":0.01723623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01525029513186154,"score_gpt":0.1895057362732205,"score_spread":0.1742554411413589,"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."}}