{"id":"W4246563707","doi":"10.1121/1.5031018.1","title":"10.1121/1.5031018.1","year":2018,"lang":"en","type":"dataset","venue":"Default Digital Object Group","topic":"Educational Robotics and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Acoustics; Kullback–Leibler divergence; Entropy (arrow of time); Bandwidth (computing); Mathematics; Random noise; Inverse; Speech recognition; Computer science; Physics; Statistics; Telecommunications","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001344371,0.0004062215,0.0003296432,0.0002429166,0.0001080085,0.001179736,0.001852159,0.0002422611,0.0003131547],"category_scores_gemma":[0.0001782753,0.0003935698,0.0001792053,0.0004587706,0.00006143095,0.0008360654,0.0005974656,0.0002977144,0.006932323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001096266,"about_ca_system_score_gemma":0.000137983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003388735,"about_ca_topic_score_gemma":0.00001275936,"domain_scores_codex":[0.9979275,0.00001303314,0.0003561474,0.0006633998,0.0005411983,0.0004987181],"domain_scores_gemma":[0.9981395,0.000154386,0.0001365315,0.001229316,0.0001281887,0.0002120951],"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":[0.000002075141,0.00008099528,0.000001104193,0.00004757815,0.00003714117,0.00002054797,0.0000138814,0.00004199221,9.320889e-7,0.00059793,0.9976659,0.001489966],"study_design_scores_gemma":[0.0001009911,0.0001426534,0.00004630526,0.00006692994,0.00001137813,0.00004129305,0.000002873507,0.000943327,0.00000377878,0.0005390263,0.9976256,0.000475765],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001337829,0.0001680751,0.01102052,0.00009746067,0.002020958,0.0001640865,0.9800438,0.0002182441,0.006253464],"genre_scores_gemma":[0.0003326041,0.00002086722,0.002106618,0.0001822978,0.001040711,0.00003108431,0.9936975,0.00003241915,0.002555874],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01365371,"threshold_uncertainty_score":0.9998571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009741029324410778,"score_gpt":0.2332038858557676,"score_spread":0.2234628565313568,"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."}}