{"id":"W2024276620","doi":"10.1007/s11263-011-0474-7","title":"Energy-Based Geometric Multi-model Fitting","year":2011,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":314,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"RANSAC; Mathematics; Geometric data analysis; Regularization (linguistics); Algorithm; Mathematical optimization; Outlier; Data point; Computer science; Artificial intelligence","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":[0.000209611,0.0000720876,0.00008448531,0.00017555,0.00004227912,0.0000349014,0.0003919048,0.00003340287,0.0001462139],"category_scores_gemma":[0.0000115543,0.00005995542,0.00009501797,0.0001490128,0.00003965189,0.00016609,0.00009574289,0.00009485371,0.00005278748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008619393,"about_ca_system_score_gemma":0.00001582491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005412211,"about_ca_topic_score_gemma":0.000003448692,"domain_scores_codex":[0.9990586,0.00002372837,0.0002919258,0.0001092213,0.0004267422,0.00008980231],"domain_scores_gemma":[0.999456,0.00004117765,0.0002478895,0.00009991789,0.00008399961,0.00007097637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008177174,0.0005633727,0.004137474,0.000001581897,0.00004884763,0.00006383874,0.0003981686,0.09411592,0.005943349,0.0002064341,0.00673663,0.8877026],"study_design_scores_gemma":[0.0005157223,0.000106748,0.02155524,0.00003599713,0.000007520456,0.0000752392,0.00000449992,0.968272,0.003747475,0.0005507188,0.005037679,0.00009111263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1144473,0.0000108279,0.883203,0.0001854395,0.0003622297,0.00001721238,9.02852e-7,0.0000111942,0.001761928],"genre_scores_gemma":[0.7426068,0.000004667446,0.2568614,0.0003433579,0.000108846,1.089103e-7,0.000001247544,0.000005888754,0.00006767288],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8876115,"threshold_uncertainty_score":0.2444911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02462600032141308,"score_gpt":0.2704007078274928,"score_spread":0.2457747075060797,"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."}}