{"id":"W4393442459","doi":"10.5281/zenodo.4016857","title":"A Comparison of Natural Language Understanding Services for Software Bots","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Software; Natural (archaeology); Software engineering; Programming language; History; Archaeology","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":[0.001621693,0.001837616,0.0009510607,0.003414273,0.00105502,0.001177845,0.001681321,0.001947775,0.009380216],"category_scores_gemma":[0.006427841,0.0002736841,0.00120885,0.002513637,0.0005523323,0.002131102,0.00149187,0.001407503,0.009884787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001820604,"about_ca_system_score_gemma":0.001731706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03775605,"about_ca_topic_score_gemma":0.07128907,"domain_scores_codex":[0.9983093,0.0003500762,0.00015335,0.0005099326,0.0004656804,0.0002116199],"domain_scores_gemma":[0.9966271,0.001309496,0.0001814533,0.0008173201,0.0007536715,0.0003110044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002675405,0.001572986,0.01529713,0.002183806,0.0003299811,0.0004485947,0.0004093775,0.009165801,0.004793524,0.003131195,0.8765233,0.08346886],"study_design_scores_gemma":[0.00148425,0.001495108,0.1102514,0.0007584324,0.0004532362,0.00213521,0.002759812,0.1526642,0.01749869,0.008949724,0.7012872,0.0002627354],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.149554,0.002041682,0.004724253,0.001223525,0.0004369953,0.0004472539,0.8128897,0.01334487,0.01533771],"genre_scores_gemma":[0.03821121,0.0001798205,0.00443499,0.00015905,0.00002560758,0.0001542426,0.9538857,0.0002053811,0.002743996],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03775605,"threshold_uncertainty_score":0.07507259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0710544871344522,"score_gpt":0.3242120747320547,"score_spread":0.2531575875976025,"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."}}