{"id":"W1974873233","doi":"10.1142/s0219720013410059","title":"ENHANCING GENOMICS INFORMATION RETRIEVAL THROUGH DIMENSIONAL ANALYSIS","year":2013,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Université de Neuchâtel; University of Melbourne","keywords":"Computer science; Linear subspace; Information retrieval; Dimension (graph theory); Rank (graph theory); Homogeneity (statistics); Graph; Data mining; Set (abstract data type); Genomics; Theoretical computer science; Machine learning; Mathematics; Genome","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":[0.0002369936,0.0001118529,0.000261013,0.0003119642,0.0001089373,0.0001298888,0.0002788255,0.00007494717,0.00001109042],"category_scores_gemma":[0.0000465818,0.00008552198,0.0001186262,0.0005714645,0.00006708743,0.002419312,0.0001361827,0.0001712039,0.00001667916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003205629,"about_ca_system_score_gemma":0.00007475614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003746706,"about_ca_topic_score_gemma":5.616838e-7,"domain_scores_codex":[0.9986961,0.00003222568,0.0008387205,0.0000675644,0.000197243,0.0001680819],"domain_scores_gemma":[0.9983027,0.0002681934,0.0007545052,0.0001032647,0.0004874625,0.00008386401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008432917,0.00009332992,0.003000754,0.00006646062,0.001460307,0.00000615709,0.004314529,0.7032027,0.00106179,0.2016587,0.00140887,0.08364207],"study_design_scores_gemma":[0.0003917214,0.000226879,0.00560875,0.00000964079,0.00004459652,0.0001411757,0.00007701563,0.9033213,0.00018168,0.08898448,0.0008695611,0.0001431885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1260679,0.000114325,0.8728697,0.0005717751,0.0001974623,0.00007035125,0.000003364668,0.00001237266,0.00009275482],"genre_scores_gemma":[0.4963941,0.00004798692,0.5023018,0.00118973,0.0000421376,6.677351e-7,0.0000191136,0.000001856717,0.000002647609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3705679,"threshold_uncertainty_score":0.3487485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00768950690829641,"score_gpt":0.2355901914549949,"score_spread":0.2279006845466985,"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."}}