{"id":"W2490364661","doi":"10.3166/ts.27.53-78","title":"A priori par normes mixtes pour les problèmes inverses. Application à la localisation de sources en M/EEG","year":2010,"lang":"fr","type":"preprint","venue":"Traitement du signal","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Nationale de la Recherche","keywords":"Humanities; Philosophy; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007326016,0.0006260166,0.0004965169,0.0002055045,0.000200651,0.0002271094,0.0005140909,0.0008277565,0.0002888638],"category_scores_gemma":[0.00005093415,0.0006767703,0.0002379391,0.0001341051,0.0002131753,0.0001703024,0.0002155831,0.001073104,0.00006150024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003033819,"about_ca_system_score_gemma":0.0001951974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001702974,"about_ca_topic_score_gemma":0.0002738897,"domain_scores_codex":[0.9974765,0.000315621,0.000642184,0.0006200668,0.0004091593,0.0005365303],"domain_scores_gemma":[0.9985555,0.0003532336,0.0003152614,0.0004507816,0.0001658168,0.0001593501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009010897,0.0005373008,0.01806345,0.0009343146,0.0006872634,0.00003424122,0.007894406,0.1382284,0.5893078,0.03025367,0.007059076,0.20691],"study_design_scores_gemma":[0.0008998154,0.0001369771,0.04441148,0.001234996,0.0005366304,0.00005831917,0.000594085,0.3938704,0.3783077,0.02571661,0.1527082,0.001524858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5157192,0.0006870334,0.4788696,0.001240449,0.0001942796,0.001063802,0.00003676117,0.00084941,0.001339467],"genre_scores_gemma":[0.9310205,0.0004279501,0.06687441,0.00007225353,0.0008629841,0.0003686469,0.0001240663,0.0001040025,0.0001452098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4153012,"threshold_uncertainty_score":0.9995683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0121765174229517,"score_gpt":0.2269027011130257,"score_spread":0.214726183690074,"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."}}