{"id":"W2293959295","doi":"","title":"Evaluation of a Methodology for Modeling Term Relationship through Geometry: Experiments at TREC 2010 Relevance Feedback Track","year":2010,"lang":"en","type":"article","venue":"Research Padua  Archive (University of Padua)","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"European Commission","keywords":"Relevance feedback; Subspace topology; Relevance (law); Computer science; Term (time); Information retrieval; Exploit; Formalism (music); Track (disk drive); Data mining; Artificial intelligence; Image retrieval; Machine learning; Natural language processing; Image (mathematics)","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":[0.0164632,0.001241558,0.0008140129,0.001663502,0.000780196,0.001432258,0.002398613,0.00208358,0.001527742],"category_scores_gemma":[0.0365096,0.0005478698,0.0009700223,0.002004555,0.0007259435,0.002735629,0.001580638,0.001434291,0.000910557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001573987,"about_ca_system_score_gemma":0.001599848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01390236,"about_ca_topic_score_gemma":0.01273565,"domain_scores_codex":[0.9893109,0.007148672,0.0004703072,0.001534893,0.001348401,0.000186959],"domain_scores_gemma":[0.9685017,0.02455917,0.001281107,0.002201798,0.003002999,0.0004532106],"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":[0.004198836,0.00306812,0.01249762,0.001716491,0.0008820468,0.0002302735,0.001821069,0.2289614,0.04345018,0.003749398,0.01141343,0.6880111],"study_design_scores_gemma":[0.0004829218,0.002684516,0.005295245,0.00004030743,0.000137512,0.0001428922,0.0003216929,0.959299,0.0256471,0.002326704,0.003540084,0.00008208988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4420443,0.001607283,0.5331964,0.0008425947,0.0002829275,0.002505094,0.003451318,0.01260543,0.003464661],"genre_scores_gemma":[0.5381204,0.0003416104,0.455368,0.0001682978,0.00006749232,0.0008829054,0.003346092,0.0004780478,0.001227161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0164632,"threshold_uncertainty_score":0.08706677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.45329646964131,"score_gpt":0.438245661100384,"score_spread":0.01505080854092605,"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."}}