{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00528759,0.001454667,0.001557627,0.001197364,0.0008447156,0.001451396,0.003178008,0.002974618,0.0092215],"category_scores_gemma":[0.0268497,0.0006241743,0.000673834,0.00213032,0.0007216026,0.004403548,0.001823387,0.002008102,0.002762828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001328083,"about_ca_system_score_gemma":0.002066595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01001203,"about_ca_topic_score_gemma":0.007139135,"domain_scores_codex":[0.9975247,0.0009866931,0.0002272293,0.0005137013,0.0005357959,0.0002118321],"domain_scores_gemma":[0.9825532,0.01355743,0.0002829311,0.001717418,0.001588986,0.000300017],"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":[0.004512602,0.005467864,0.009874507,0.001242687,0.0004381286,0.0003534133,0.0006001504,0.2317805,0.00642992,0.01080732,0.03104986,0.6974431],"study_design_scores_gemma":[0.0009780178,0.0004254688,0.001424702,0.00004688253,0.00008238017,0.00009551922,0.0001803625,0.9816638,0.004404127,0.007859108,0.002805456,0.00003416669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6400386,0.003992807,0.2972321,0.002084834,0.0006197609,0.002731104,0.003175063,0.02346424,0.02666141],"genre_scores_gemma":[0.5441714,0.0006897709,0.4442716,0.0004548565,0.00007259358,0.001716669,0.003658497,0.000847284,0.004117365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01001203,"threshold_uncertainty_score":0.03084898,"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."}}