{"id":"W4302971779","doi":"10.7287/peerj.preprints.3007v1","title":"Multi-label classification of frog species via deep learning","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Binary classification; Feature (linguistics); Pattern recognition (psychology); Relevance (law); Binary number; Machine learning; Mathematics; Support vector machine","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":[0.0007048845,0.0006691451,0.0005495098,0.001603746,0.0003498858,0.0005394375,0.0008875368,0.0009810835,0.001863154],"category_scores_gemma":[0.001186254,0.0001967242,0.000743276,0.0006733866,0.0003361476,0.001014821,0.0009048739,0.0009941999,0.000735239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004294792,"about_ca_system_score_gemma":0.0002934668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001287662,"about_ca_topic_score_gemma":0.002460467,"domain_scores_codex":[0.999534,0.00009776711,0.00002431838,0.0001580404,0.00009399588,0.00009183581],"domain_scores_gemma":[0.9993497,0.0002262082,0.000122781,0.00009516127,0.0001315212,0.00007462885],"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":[0.0005384198,0.0004696714,0.01715749,0.0001912218,0.0001311815,0.0003112631,0.0001993677,0.05652193,0.1412158,0.002447882,0.004092271,0.7767234],"study_design_scores_gemma":[0.00001537254,0.0001169064,0.007756659,0.00002008608,0.00003382157,0.0001607225,0.00009242797,0.9678706,0.01871297,0.003955884,0.001236077,0.00002844929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.406391,0.00123383,0.5846939,0.0004056614,0.0001890785,0.0001036096,0.000675634,0.00232425,0.003983068],"genre_scores_gemma":[0.821221,0.0002680213,0.1727832,0.0001940051,0.00009954566,0.00007917444,0.00135039,0.00008253176,0.003922217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001863154,"threshold_uncertainty_score":0.006232858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1115273491388944,"score_gpt":0.3521613884071325,"score_spread":0.2406340392682381,"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."}}