{"id":"W2402131239","doi":"","title":"Using Stream Features for Instant Document Ranking.","year":2013,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Instant; Ranking (information retrieval); Computer science; Information retrieval; Biology; Food science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001502176,0.001219125,0.001480983,0.007620594,0.0006014738,0.002369244,0.001081815,0.001191427,0.00689679],"category_scores_gemma":[0.00814811,0.0003557125,0.0007234452,0.005498448,0.0001866151,0.003453563,0.000770733,0.0009969268,0.006310312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005193764,"about_ca_system_score_gemma":0.0009280625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006239112,"about_ca_topic_score_gemma":0.01127217,"domain_scores_codex":[0.9987268,0.0002601949,0.000133,0.0002124041,0.0005202902,0.0001472812],"domain_scores_gemma":[0.9955503,0.001890522,0.0003320343,0.0006595121,0.001326233,0.0002413618],"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":[0.00244441,0.0004554152,0.005549107,0.0004237486,0.0002177407,0.0002177007,0.00007639293,0.008657623,0.01485522,0.002333083,0.09060733,0.8741623],"study_design_scores_gemma":[0.000556799,0.001327899,0.01222334,0.0001136263,0.0003756874,0.0008451581,0.0003462649,0.8561141,0.04936957,0.0221512,0.05641197,0.0001643978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1665869,0.01079226,0.6837096,0.001752198,0.003503971,0.001028687,0.04257726,0.07571758,0.0143315],"genre_scores_gemma":[0.5046571,0.002100562,0.4249919,0.0003523761,0.001788429,0.0004481983,0.04635348,0.001343033,0.01796486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007620594,"threshold_uncertainty_score":0.02307206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01928595445713802,"score_gpt":0.258074460615428,"score_spread":0.23878850615829,"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."}}