{"id":"W4393284460","doi":"10.48550/arxiv.2403.17381","title":"Application-Driven Innovation in Machine Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Biological Infrastructure; Office of International Science and Engineering; Abdul Latif Jameel Water and Food Systems Lab, Massachusetts Institute of Technology; Harvard Data Science Initiative, Harvard University; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002561463,0.0002612493,0.0002348633,0.001069723,0.00009338155,0.0002293597,0.0006156936,0.0002402166,0.0001985133],"category_scores_gemma":[0.00007948047,0.0002957773,0.00006908345,0.002705073,0.00006450433,0.0004722966,0.001859027,0.0009382919,0.001224467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001113362,"about_ca_system_score_gemma":0.0000517011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001975763,"about_ca_topic_score_gemma":0.0005367268,"domain_scores_codex":[0.9985984,0.00001183136,0.0002923397,0.000781243,0.0000826759,0.0002335261],"domain_scores_gemma":[0.9990603,0.00002583064,0.0002863769,0.0004017306,0.0002175956,0.000008200534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004938318,0.0001035342,0.08471578,0.001056222,0.00005162295,0.0001227358,0.00003948742,0.2375365,0.0001808776,0.6702401,0.0008766816,0.005027084],"study_design_scores_gemma":[0.0001598078,0.000002731241,0.002733666,0.00024425,0.00008072025,7.835928e-7,0.00006077576,0.8534394,0.00002153603,0.1070857,0.03575308,0.0004176126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8906242,0.0002307456,0.07727765,0.000694809,0.001233321,0.0007441525,0.00003412161,0.0006498917,0.02851108],"genre_scores_gemma":[0.9970844,0.00007698467,0.00004369399,0.0001874293,0.0004376741,0.000003659012,0.0005994109,0.00003380433,0.00153291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6159029,"threshold_uncertainty_score":0.9999495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1061853734691915,"score_gpt":0.2201274122885152,"score_spread":0.1139420388193237,"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."}}