{"id":"W2104080300","doi":"10.1186/gb-2003-4-3-r23","title":"The GRID: The General Repository for Interaction Datasets","year":2003,"lang":"en","type":"article","venue":"Genome biology","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":299,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Grid; Computer science; Parsing; Visualization; Genome browser; Information retrieval; Data mining; Biology; Genomics; Artificial intelligence; Genome; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003570131,0.0001020144,0.00007581845,0.00000967044,0.0003947171,0.00003515427,0.0002498119,0.0001207103,0.000004991074],"category_scores_gemma":[0.00004191283,0.00005678822,0.00007196161,0.00002800984,0.0001182466,0.000002040672,0.00006652096,0.00008069212,0.000008953842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001185076,"about_ca_system_score_gemma":0.00004141833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007048364,"about_ca_topic_score_gemma":0.00002169948,"domain_scores_codex":[0.9992638,0.00008136559,0.0002057743,0.0001739438,0.00002573623,0.0002494044],"domain_scores_gemma":[0.9993569,0.00004277908,0.0001016992,0.0004307434,0.0000336877,0.00003417506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003523433,0.0000636575,0.0005982475,0.00002243543,0.0004503065,0.000001300059,0.0001721727,0.000443141,0.7710884,0.04425691,0.1609106,0.0216405],"study_design_scores_gemma":[0.0001935816,0.0001471747,0.0001793961,6.803618e-7,0.0000111642,0.00004075182,0.00006482888,0.0001194686,0.005434428,0.001086302,0.9926307,0.00009149279],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7817855,0.0354161,0.1127734,0.004647784,0.0241263,0.004461449,0.001932312,0.00008958994,0.03476752],"genre_scores_gemma":[0.9893449,0.0006394453,0.001400837,0.001627784,0.002240243,0.0001790607,0.002241687,0.00002576353,0.00230027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8317201,"threshold_uncertainty_score":0.3035884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009498871322888507,"score_gpt":0.2576102786892654,"score_spread":0.2481114073663769,"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."}}