{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009806596,0.00009338254,0.00009455199,0.00008967702,0.00009754257,0.00007481995,0.0003811778,0.00006138535,0.00001090481],"category_scores_gemma":[0.00005725881,0.00008132533,0.00004727424,0.0002846535,0.00002052093,0.0005511572,0.00008463561,0.00006962544,0.000004176963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005808306,"about_ca_system_score_gemma":0.00002067349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000160725,"about_ca_topic_score_gemma":0.00003278345,"domain_scores_codex":[0.9992937,0.00001004725,0.0001448649,0.0002563768,0.0001081649,0.0001868145],"domain_scores_gemma":[0.9993387,0.0001039061,0.00005481829,0.000323352,0.0001270047,0.0000522588],"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":[0.00002020195,0.0001005737,0.0001669352,0.00003564676,0.00001747537,0.00000586171,0.0001747387,0.0000639061,0.1970975,0.2837854,0.009901269,0.5086305],"study_design_scores_gemma":[0.0002634939,0.0001041641,0.00002133521,0.00001067752,0.000005603592,0.00001416922,0.000004672246,0.1856686,0.6784709,0.07952227,0.05567668,0.0002373994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004341818,0.00003764598,0.9974766,0.0003659958,0.0002428174,0.000110274,6.657976e-7,0.0004541812,0.0008775725],"genre_scores_gemma":[0.1819477,0.00003513343,0.8157381,0.0006647606,0.0000724799,0.00002532457,0.000004550825,0.00001131167,0.001500657],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.508393,"threshold_uncertainty_score":0.3316351,"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."}}