{"id":"W7043846155","doi":"","title":"Traitement anti-angiogène par des vecteurs nanosphériques bioadhésifs","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":"Context (archaeology); Selection (genetic algorithm); MEDLINE","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":[0.0002410293,0.0004762067,0.0004312558,0.0002594349,0.0004500633,0.0006877306,0.0002778648,0.0005886175,0.007249906],"category_scores_gemma":[0.0003782985,0.0002147194,0.0004728215,0.0001333142,0.0003212282,0.000306494,0.0002956708,0.0005990583,0.002542872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003712281,"about_ca_system_score_gemma":0.0003620517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009103878,"about_ca_topic_score_gemma":0.001228291,"domain_scores_codex":[0.9997676,0.00002216274,0.00001314169,0.00005787981,0.000103314,0.00003588626],"domain_scores_gemma":[0.9998077,0.00004988714,0.00003616745,0.00002922961,0.00005806441,0.00001910059],"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":[0.0005949866,0.00009761754,0.0005184043,0.0001508296,0.00003481879,0.0003325054,0.00008442818,0.0004531505,0.9577339,0.001012889,0.002441513,0.03654499],"study_design_scores_gemma":[0.00006584468,0.0007360659,0.001999596,0.00001853257,0.00006673211,0.0008055067,0.00005037796,0.003153939,0.9506698,0.000297776,0.04211591,0.00001990078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8566225,0.01605233,0.0611661,0.00170707,0.003327689,0.0003397414,0.0007230216,0.001978947,0.05808253],"genre_scores_gemma":[0.8511558,0.004757633,0.01598317,0.0008433696,0.0003468738,0.0002004712,0.000554113,0.0003510765,0.1258074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007249906,"threshold_uncertainty_score":0.02425331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004838889173505247,"score_gpt":0.1497002510860555,"score_spread":0.1448613619125503,"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."}}