{"id":"W2167609405","doi":"10.1613/jair.2693","title":"The Latent Relation Mapping Engine: Algorithm and Experiments","year":2008,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Topic Modeling","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Princeton University","keywords":"Analogy; Relation (database); Core (optical fiber); Set (abstract data type); Latent semantic analysis; Variety (cybernetics); Relational database; Raw data","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":[0.002802339,0.00006350524,0.0001008891,0.0002104676,0.0005825163,0.0001829822,0.0006110411,0.00004369311,0.000005233038],"category_scores_gemma":[0.0002961662,0.00004366144,0.00004486589,0.0003969807,0.0001428204,0.0004213786,0.0001960297,0.0004813597,0.00001909515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007800399,"about_ca_system_score_gemma":0.000132142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001954957,"about_ca_topic_score_gemma":0.000001103009,"domain_scores_codex":[0.9980669,0.000181909,0.0004774265,0.0001463952,0.0008295553,0.0002978084],"domain_scores_gemma":[0.9986466,0.000398945,0.0001161981,0.0002307259,0.000493866,0.0001136482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009875154,0.0000381707,0.0001510797,0.000002789919,0.00001570207,0.00007693371,0.003766167,0.001115329,0.002806059,0.01389514,0.0001068994,0.9780158],"study_design_scores_gemma":[0.00003676464,0.0001973601,0.000733426,0.00005611327,0.000001376283,0.0003204475,0.001053914,0.9290766,0.03429125,0.03241746,0.001713158,0.0001021276],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08787469,0.001050761,0.9089587,0.00157949,0.000341218,0.00008251892,9.243689e-8,0.000009084917,0.0001034267],"genre_scores_gemma":[0.8989751,0.001304174,0.09923895,0.000018472,0.0003242186,0.000003093928,6.623928e-8,0.000005979722,0.0001299641],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9779137,"threshold_uncertainty_score":0.4480304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2756728393136693,"score_gpt":0.4075134273255451,"score_spread":0.1318405880118758,"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."}}