{"id":"W3035293706","doi":"10.1126/science.368.6496.1174","title":"Computing cancer's weak spots","year":2020,"lang":"en","type":"article","venue":"Science","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Spots; Cancer; Computational biology; Biology; Genetics; Botany","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":[0.001777224,0.001177788,0.001539855,0.003290178,0.001419461,0.002010176,0.001913602,0.001801995,0.005434297],"category_scores_gemma":[0.007368726,0.0004931599,0.001443498,0.002343122,0.0009139181,0.001265188,0.001647223,0.001044957,0.001910839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008067452,"about_ca_system_score_gemma":0.002235174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004247345,"about_ca_topic_score_gemma":0.005668252,"domain_scores_codex":[0.9991308,0.0001193695,0.0000814708,0.0002949817,0.0002324376,0.0001409494],"domain_scores_gemma":[0.9979799,0.0009784858,0.0001616027,0.0002233002,0.0005105956,0.0001461528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002831204,0.0004304127,0.03709156,0.0005132434,0.0004492946,0.000448643,0.0003015843,0.1183554,0.01754168,0.01526925,0.03946408,0.7673036],"study_design_scores_gemma":[0.0002235658,0.0003654543,0.005723802,0.00005261914,0.0002119751,0.0005063445,0.0002177885,0.9354132,0.01176508,0.03655979,0.008921026,0.00003935887],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2904041,0.002640914,0.6832435,0.002762928,0.0005363787,0.0005138071,0.003881463,0.00907942,0.006937637],"genre_scores_gemma":[0.491308,0.000529707,0.4912233,0.000620865,0.0002665679,0.0004291638,0.007220802,0.0004865188,0.00791504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005434297,"threshold_uncertainty_score":0.01817954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06835083280443924,"score_gpt":0.387561688716197,"score_spread":0.3192108559117577,"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."}}