{"id":"W1990228223","doi":"10.1126/scisignal.137pl2","title":"Analysis of Signaling Events by Combining High-Throughput Screening Technology with Computer-Based Image Analysis","year":2008,"lang":"en","type":"article","venue":"Science Signaling","topic":"Ubiquitin and proteasome pathways","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Throughput; Computer science; Computational biology; High-content screening; Bioinformatics; Biology; Genetics; Telecommunications; Cell; Wireless","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008077175,0.0002268176,0.0004889984,0.001113892,0.000487873,0.00003258673,0.0006225631,0.0001244385,0.00004069592],"category_scores_gemma":[0.00005986805,0.0002074867,0.0002236439,0.00656788,0.0008796428,0.00002266645,0.0001601302,0.0001470929,0.000001619535],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002350324,"about_ca_system_score_gemma":0.0002100754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008695411,"about_ca_topic_score_gemma":0.000009234842,"domain_scores_codex":[0.9977227,0.00007396605,0.0003956666,0.0007610387,0.0005480714,0.0004985288],"domain_scores_gemma":[0.9986442,0.00003928256,0.0003321775,0.0005056683,0.0003538246,0.0001248943],"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":[0.0000389052,0.00006082889,0.04739091,0.000007885129,0.0007915189,0.000007111467,0.00007409292,0.05006755,0.9011431,0.00003101098,0.000006004091,0.0003810536],"study_design_scores_gemma":[0.0004804813,0.0003792025,0.00142346,0.00003165347,0.0008056165,0.000004547513,0.000116613,0.02667359,0.9697539,0.00002200991,0.00002851982,0.000280361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6279467,0.0001005715,0.3717263,0.00003401148,0.00001971829,0.00008795477,0.00002790772,0.00002499997,0.00003185922],"genre_scores_gemma":[0.8981985,0.000005225719,0.1014902,0.00009272985,0.00003034131,0.00001151851,0.0001418783,0.0000149876,0.00001458006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2702519,"threshold_uncertainty_score":0.8461062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01225469407744651,"score_gpt":0.246718843959628,"score_spread":0.2344641498821815,"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."}}