{"id":"W6996498384","doi":"","title":"Segmentation d'images de texture par des modÃ¨les multirÃ©solutions","year":2000,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Texture (cosmology); Segmentation; Pattern recognition (psychology); Image segmentation; Feature (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004537777,0.001822965,0.0008815024,0.002038128,0.0006209704,0.002127298,0.0008563962,0.001435957,0.01490788],"category_scores_gemma":[0.0008336905,0.000671141,0.00115301,0.001186324,0.0006313599,0.0006201135,0.0006317511,0.0008075513,0.005061667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255908,"about_ca_system_score_gemma":0.001304748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02954504,"about_ca_topic_score_gemma":0.03679253,"domain_scores_codex":[0.9994019,0.00005380042,0.00002162193,0.0001195924,0.0003218438,0.00008131279],"domain_scores_gemma":[0.9995257,0.0001369265,0.00002874897,0.00007401241,0.000185597,0.00004904684],"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":[0.0009852856,0.0001563564,0.001686057,0.0003252261,0.0001387057,0.0003725612,0.0003618122,0.04936335,0.3712681,0.004230489,0.01466206,0.55645],"study_design_scores_gemma":[0.0001507418,0.0002783977,0.01250502,0.00009816134,0.00008792466,0.0008835296,0.0003632956,0.7220743,0.2166281,0.00279892,0.04404893,0.00008267669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1398906,0.001072414,0.8250602,0.000701742,0.0003113699,0.0002234439,0.001464458,0.009964589,0.02131099],"genre_scores_gemma":[0.2564566,0.0007743335,0.6929845,0.0001929375,0.0001171458,0.0001321128,0.003093538,0.002351928,0.04389697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02954504,"threshold_uncertainty_score":0.05874616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004785055929377386,"score_gpt":0.1548720063426043,"score_spread":0.1500869504132269,"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."}}