{"id":"W6989347057","doi":"","title":"Application du SPR dans le criblage des ligands synthÃ©tiques du CD36 et sa validation","year":2012,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"HT1080; Filter (signal processing); Limiting","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":[0.00365804,0.001300752,0.001105401,0.0009384092,0.0008878286,0.001457742,0.0006603616,0.001404577,0.003660726],"category_scores_gemma":[0.001782081,0.0005781201,0.001401968,0.0006916592,0.0008654625,0.0006433097,0.0007891539,0.00157004,0.002399155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009514771,"about_ca_system_score_gemma":0.001319165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002568158,"about_ca_topic_score_gemma":0.002635848,"domain_scores_codex":[0.9976832,0.0005237854,0.0001325449,0.0004258575,0.0009095142,0.0003251892],"domain_scores_gemma":[0.9990724,0.0002649481,0.00009377846,0.0001116343,0.0003562592,0.0001010547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003142748,0.0001725153,0.0003861247,0.0005319092,0.00002391111,0.0001267623,0.0001809837,0.001621999,0.9838217,0.0003145582,0.0004045136,0.01210067],"study_design_scores_gemma":[0.00003313028,0.001110254,0.001025979,0.00008000933,0.00004818248,0.0003050552,0.0001466503,0.006564051,0.9693429,0.0002012592,0.02110746,0.00003509839],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7975,0.02129599,0.1625684,0.001059609,0.000576096,0.001705502,0.002378213,0.001519614,0.01139655],"genre_scores_gemma":[0.7070162,0.01803902,0.2329659,0.0008798626,0.0001155244,0.002335362,0.0041829,0.000439518,0.03402577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003660726,"threshold_uncertainty_score":0.01934582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003169998433510797,"score_gpt":0.1480558890311745,"score_spread":0.1448858905976637,"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."}}