{"id":"W1492179299","doi":"10.1007/978-3-642-10331-5_42","title":"Maximum Likelihood Estimation Sample Consensus with Validation of Individual Correspondences","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"RANSAC; Estimator; Computer science; Sample (material); Statistics; Estimation; Sequence (biology); Algorithm; Maximum likelihood; Artificial intelligence; Mathematics; Pattern recognition (psychology); Image (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":[0.008811858,0.00171668,0.003133155,0.00143486,0.001374577,0.001941276,0.004467994,0.004399102,0.003134887],"category_scores_gemma":[0.03258109,0.001655035,0.001889227,0.001741178,0.002367537,0.003446246,0.004749284,0.00286716,0.002550846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009367137,"about_ca_system_score_gemma":0.002043683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002817336,"about_ca_topic_score_gemma":0.003307466,"domain_scores_codex":[0.9938713,0.002658554,0.0003186544,0.001522088,0.001283318,0.0003459661],"domain_scores_gemma":[0.9764035,0.01287548,0.001190658,0.005408079,0.00382889,0.0002933889],"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":[0.0006807541,0.000192625,0.002141229,0.0002693475,0.0002974847,0.000158761,0.0002987435,0.6191665,0.01270054,0.0121675,0.002533064,0.3493934],"study_design_scores_gemma":[0.00002230713,0.00005238775,0.0003253293,0.000008737671,0.00001665261,0.00003987518,0.0000221789,0.987407,0.004139977,0.007674335,0.0002771457,0.00001412119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009860599,0.00006367908,0.9889296,0.00004599194,0.0000195236,0.00003397507,0.00003129912,0.0005496672,0.0004657668],"genre_scores_gemma":[0.4016966,0.00007488765,0.5926775,0.0001047286,0.00005251892,0.0002410588,0.0007742539,0.0004679853,0.003910483],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008811858,"threshold_uncertainty_score":0.04660213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01831767387631549,"score_gpt":0.2349842024208139,"score_spread":0.2166665285444984,"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."}}