{"id":"W6947939700","doi":"10.48550/arxiv.0812.4446","title":"The Latent Relation Mapping Engine: Algorithm and Experiments","year":2008,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Relation (database); Analogy; Core (optical fiber); Set (abstract data type); Variety (cybernetics); Latent semantic analysis; 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":[0.005349441,0.001547949,0.001648653,0.001281997,0.0008927678,0.00156252,0.00351532,0.003298498,0.00996022],"category_scores_gemma":[0.02772475,0.0006652597,0.0007341239,0.002302097,0.0008378325,0.004519445,0.002034012,0.00219147,0.003143159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001357163,"about_ca_system_score_gemma":0.002159103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009328485,"about_ca_topic_score_gemma":0.006883631,"domain_scores_codex":[0.9972171,0.001054565,0.0002523352,0.0006147719,0.0006138442,0.0002473362],"domain_scores_gemma":[0.9820411,0.0136942,0.0002937248,0.002003671,0.001644495,0.0003228303],"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":[0.005174899,0.005823703,0.01032325,0.001389361,0.0004815073,0.0004027278,0.0005520693,0.2314118,0.007219494,0.01131729,0.03863047,0.6872734],"study_design_scores_gemma":[0.001236775,0.0005056412,0.001667332,0.00005599834,0.00009083232,0.0001197128,0.0001931118,0.9768205,0.005367631,0.01049323,0.003408531,0.0000406452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6367097,0.00478992,0.2923005,0.002496958,0.0007334768,0.002634966,0.003891126,0.02694765,0.02949571],"genre_scores_gemma":[0.5434613,0.0007330829,0.4434162,0.0005587387,0.00008513539,0.001656081,0.004460307,0.001043741,0.004585445],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00996022,"threshold_uncertainty_score":0.03332031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08281923177864406,"score_gpt":0.1996064879468029,"score_spread":0.1167872561681588,"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."}}