{"id":"W6976560260","doi":"10.60692/7b72h-dk987","title":"BIRD: Big Impulse Response Dataset","year":2020,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Human-Animal Interaction Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Python (programming language); Impulse response; Code (set theory); Deep learning; Software; Open source; Download","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":[0.0004859151,0.002391496,0.001006088,0.001071356,0.0005162957,0.0009197665,0.002086132,0.001773665,0.02460376],"category_scores_gemma":[0.001918983,0.0004556059,0.001172266,0.001085695,0.0003335032,0.000625756,0.001257204,0.001744403,0.0306219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005032315,"about_ca_system_score_gemma":0.000744208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007920769,"about_ca_topic_score_gemma":0.02103909,"domain_scores_codex":[0.9995182,0.00007407046,0.00003163536,0.0001449267,0.0001455369,0.00008571176],"domain_scores_gemma":[0.9994503,0.0001164604,0.0000401742,0.0001639444,0.0001539736,0.00007528003],"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":[0.0004396335,0.000260531,0.002886281,0.000684024,0.0001433872,0.0001845599,0.00004596695,0.003894592,0.004874927,0.0006231025,0.9570729,0.02889005],"study_design_scores_gemma":[0.0007627129,0.0005977617,0.04354376,0.0004999937,0.0002159528,0.001445722,0.0003325698,0.05265322,0.02058861,0.007386491,0.8715104,0.0004627605],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009620584,0.0005706754,0.009227814,0.0003024325,0.0004488834,0.0001429447,0.9634996,0.01175192,0.004435139],"genre_scores_gemma":[0.01232439,0.0001644761,0.006791965,0.0002232225,0.00007774733,0.0002724937,0.9763911,0.0005126899,0.003241789],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02460376,"threshold_uncertainty_score":0.08230776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0493884204148911,"score_gpt":0.2877316645481722,"score_spread":0.2383432441332811,"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."}}