{"id":"W4246573698","doi":"10.3410/f.1028542.342180","title":"Faculty Opinions recommendation of Inferring protein domain interactions from databases of interacting proteins.","year":2005,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Domain (mathematical analysis); Database; Computer science; Data science; Information retrieval; Computational biology; Biology; Mathematics","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":[0.00179555,0.004417176,0.002211748,0.005675973,0.001045057,0.003113928,0.004673045,0.003651168,0.05139494],"category_scores_gemma":[0.00837001,0.0013065,0.001883022,0.006803532,0.00039363,0.00210872,0.001823065,0.002604696,0.05172484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001860075,"about_ca_system_score_gemma":0.004200867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03224132,"about_ca_topic_score_gemma":0.08761272,"domain_scores_codex":[0.9983727,0.0002381883,0.000155308,0.0004828208,0.0005742291,0.0001767756],"domain_scores_gemma":[0.996467,0.0008087867,0.0003123124,0.0009455332,0.0007830251,0.000683334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001156416,0.00007074533,0.0009709934,0.0004908431,0.0000701654,0.00003264899,0.00001120594,0.0003830351,0.000272705,0.0003254204,0.9931743,0.004082344],"study_design_scores_gemma":[0.0007220422,0.00006515207,0.008461135,0.0002989981,0.0001757723,0.0001940848,0.00007131882,0.007768024,0.002685167,0.002117975,0.9773775,0.00006282343],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007071572,0.0002099727,0.0004899288,0.000218113,0.00006363401,0.00003908693,0.9939546,0.002465137,0.001852313],"genre_scores_gemma":[0.000613975,0.00007321548,0.001196752,0.00005897938,0.000007336606,0.00003316379,0.9971361,0.00007935483,0.0008010256],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05139494,"threshold_uncertainty_score":0.1719332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04392047593620314,"score_gpt":0.3812518414973441,"score_spread":0.337331365561141,"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."}}