{"id":"W2081631398","doi":"10.1145/2037676.2037689","title":"Contextual tag inference","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Music and Audio Processing","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Division of Information and Intelligent Systems","keywords":"CLIPS; Computer science; Context (archaeology); Inference; Rank (graph theory); Artificial intelligence; Machine learning; Natural language processing; Support vector machine; Information retrieval; Ranking (information retrieval); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002166901,0.0001798373,0.000170325,0.000158119,0.001155913,0.0001254429,0.002426247,0.0000782166,0.00002954422],"category_scores_gemma":[0.00003292592,0.0001810646,0.00005949637,0.0005426808,0.0003034461,0.0003087639,0.000182228,0.0003661307,0.00008334043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002444582,"about_ca_system_score_gemma":0.00007463404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007448439,"about_ca_topic_score_gemma":0.00002213075,"domain_scores_codex":[0.9987952,0.00007933878,0.0003398915,0.0003984552,0.0001423134,0.0002448723],"domain_scores_gemma":[0.996131,0.0007190998,0.0001409726,0.002712392,0.0001454041,0.0001511391],"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":[0.000001833151,0.0002977203,0.0001220939,0.000008514283,0.00002154974,2.743817e-7,0.002345058,0.00002702314,0.0002265741,0.02413512,0.0000339623,0.9727803],"study_design_scores_gemma":[0.002482959,0.0003806236,0.0103264,0.0003257985,0.0001547712,0.00008901842,0.001661664,0.8210081,0.006033188,0.05044385,0.1050674,0.00202624],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007524474,0.000225114,0.9928812,0.001637549,0.00005826491,0.0003368876,0.000008343776,0.0003630692,0.003737117],"genre_scores_gemma":[0.6035216,0.0001841553,0.3956406,0.0004496963,0.00001876276,0.000115512,0.000005503942,0.00000873479,0.00005542473],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.970754,"threshold_uncertainty_score":0.8890465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07077493053295884,"score_gpt":0.304747744114829,"score_spread":0.2339728135818702,"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."}}