{"id":"W3048972944","doi":"","title":"Chips image processing to predict pulp brightness using fuzzy logic techniques","year":2005,"lang":"en","type":"other","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre de Recherche Industrielle du Québec; Polytechnique Montréal","funders":"","keywords":"Fuzzy logic; Image processing; Artificial intelligence; Computer vision; Brightness; Computer science; Mathematics; Image (mathematics); Optics","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":[0.000452933,0.0005786868,0.0004453239,0.001617428,0.0004508022,0.0007852198,0.0005783659,0.0006975433,0.004891548],"category_scores_gemma":[0.0009033405,0.0003364381,0.0005711085,0.0008378868,0.0002481844,0.000569483,0.0002554703,0.0005142638,0.001110946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007167841,"about_ca_system_score_gemma":0.0005143812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009087995,"about_ca_topic_score_gemma":0.01536352,"domain_scores_codex":[0.9998017,0.00001824735,0.00001007032,0.00004941203,0.00009179394,0.00002884392],"domain_scores_gemma":[0.9996012,0.0001249867,0.00002621826,0.00002967438,0.0002043272,0.00001357007],"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":[0.0007318231,0.0003154362,0.006847991,0.0001707334,0.00009380592,0.0001426369,0.0001039567,0.05914735,0.1498252,0.002002708,0.003610627,0.7770077],"study_design_scores_gemma":[0.00002397675,0.0001532271,0.007505191,0.00002174139,0.00007248297,0.0001058209,0.00006424839,0.894984,0.09415755,0.001000808,0.001876647,0.00003428545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1946476,0.0004861555,0.792021,0.0001747806,0.0001510265,0.0001565712,0.0004698132,0.002858095,0.009034911],"genre_scores_gemma":[0.5981857,0.0002813785,0.3890402,0.00009320801,0.00003444299,0.0001142532,0.0004733107,0.0001154659,0.01166208],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009087995,"threshold_uncertainty_score":0.01807016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101981294276985,"score_gpt":0.2518984627759578,"score_spread":0.230878649833188,"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."}}