{"id":"W1969190315","doi":"10.1109/iros.2010.5649952","title":"Energy minimization via graph cuts for semantic place labeling","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Minification; Semantics (computer science); Classification of discontinuities; Smoothness; Cut; Artificial intelligence; Graph; Energy minimization; Set (abstract data type); Energy (signal processing); Data mining; Machine learning; Pattern recognition (psychology); Image (mathematics); Theoretical computer science; Image segmentation; Mathematics","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":[0.001813237,0.001936371,0.001971554,0.002625021,0.0009699608,0.001657096,0.002677225,0.002653735,0.00279433],"category_scores_gemma":[0.00472259,0.001053925,0.001689607,0.00259753,0.001630161,0.002298574,0.001718521,0.002928919,0.0007956343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001874183,"about_ca_system_score_gemma":0.001419301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005717194,"about_ca_topic_score_gemma":0.006273623,"domain_scores_codex":[0.9982272,0.0005232588,0.00007665512,0.0004337055,0.0006194767,0.0001196726],"domain_scores_gemma":[0.9986529,0.0007702025,0.0001452753,0.0001450907,0.000238337,0.00004815271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001468101,0.00009678562,0.0004771929,0.000258232,0.000106661,0.0001144446,0.0001517888,0.7190242,0.007346916,0.04924453,0.005460597,0.2175718],"study_design_scores_gemma":[0.00001462835,0.00003265224,0.000135662,0.00001583097,0.00001359306,0.0000620494,0.00003536864,0.9520964,0.002327121,0.0432402,0.002008833,0.00001765553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001716336,0.00007578936,0.9974352,0.00007390101,0.00001828519,0.00003007493,0.00004715203,0.0002006592,0.0004026997],"genre_scores_gemma":[0.07448804,0.0001863958,0.9221899,0.000107278,0.0000677433,0.0002279279,0.0005582683,0.0004365769,0.001737915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005717194,"threshold_uncertainty_score":0.0135982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008197510260381,"score_gpt":0.2649865523306319,"score_spread":0.2549045772280281,"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."}}