{"id":"W4287668178","doi":"10.48550/arxiv.2009.09930","title":"AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive\\n Short-texts Analysis","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software Engineering Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; Kelowna General Hospital","funders":"","keywords":"Computer science; Topic model; Inference; Online algorithm; Mobile apps; Information retrieval; Word (group theory); Data mining; Machine learning; Data science; Artificial intelligence; World Wide Web; Algorithm","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":[0.003773793,0.001979583,0.002019128,0.003408377,0.0009040168,0.001898537,0.003470692,0.002249717,0.003481675],"category_scores_gemma":[0.01265861,0.00107586,0.002681741,0.003275526,0.0007227968,0.003607723,0.002696242,0.003740412,0.003266227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009986279,"about_ca_system_score_gemma":0.001671412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009888488,"about_ca_topic_score_gemma":0.01239368,"domain_scores_codex":[0.9970277,0.001063052,0.000297513,0.0009394856,0.0004744171,0.0001977768],"domain_scores_gemma":[0.9940574,0.00396314,0.000466137,0.0006113906,0.0006974686,0.000204322],"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":[0.001171051,0.0006750919,0.009307012,0.0009825688,0.0006610197,0.0004731578,0.001478864,0.1453849,0.01143595,0.01680231,0.02453811,0.7870899],"study_design_scores_gemma":[0.00003609079,0.00005847229,0.0009965914,0.00002454329,0.00004883787,0.00006710253,0.00007219146,0.9848461,0.001062289,0.008889064,0.003873273,0.00002539919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01781066,0.001900225,0.9717047,0.000540428,0.0002525017,0.0003569943,0.002036256,0.004529278,0.0008690459],"genre_scores_gemma":[0.3599302,0.002830121,0.6049336,0.0009675226,0.001569731,0.002443685,0.0165942,0.00104697,0.009683866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009888488,"threshold_uncertainty_score":0.01995796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1468090101183452,"score_gpt":0.2363875999983627,"score_spread":0.08957858988001752,"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."}}