{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001273351,0.0004598773,0.0007721763,0.0003260104,0.0001602764,0.00007601181,0.001499107,0.0005016765,0.0003714457],"category_scores_gemma":[0.011774,0.000317526,0.0005337963,0.000836409,0.0004849145,0.00007186972,0.001155247,0.0009591832,0.00002136245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007019055,"about_ca_system_score_gemma":0.0006415689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002318582,"about_ca_topic_score_gemma":0.00007532731,"domain_scores_codex":[0.9952914,0.0004048323,0.002048316,0.000579387,0.001302508,0.0003735368],"domain_scores_gemma":[0.9920936,0.0001181267,0.001957359,0.001509763,0.004037818,0.000283375],"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":[0.00004376623,0.0004397449,0.000007641296,0.004736966,0.0002118971,1.279419e-7,0.00009030807,2.088732e-7,0.0008520132,0.00002571942,0.9874718,0.006119875],"study_design_scores_gemma":[0.0005695815,0.0002267507,0.0002658768,0.01151972,0.000080799,0.00001084901,0.0001034007,0.00001524012,0.003008427,0.00001250433,0.9839,0.0002869096],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003773571,0.0006553162,0.0003832447,0.0369996,0.000532656,0.001668523,0.9596329,0.00001188612,0.00007811905],"genre_scores_gemma":[0.0001063219,0.0004050907,0.006547112,0.000723027,0.0005512431,0.0002401556,0.9909276,0.00002510217,0.0004743444],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03627657,"threshold_uncertainty_score":0.9999277,"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."}}