{"id":"W6929423502","doi":"10.48550/arxiv.1704.02744","title":"Finding strong lenses in CFHTLS using convolutional neural networks","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Nausea and vomiting management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Lens (geology); Pattern recognition (psychology); Artificial neural network; Completeness (order theory); Set (abstract data type); Data set; Identification (biology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004956817,0.0006340094,0.0002298862,0.001175209,0.0003802885,0.0007022755,0.0005320074,0.0004912358,0.0007708186],"category_scores_gemma":[0.001748362,0.0002965321,0.0005178918,0.0006420849,0.000438658,0.0006765028,0.0006719954,0.0003071812,0.0002068881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00134949,"about_ca_system_score_gemma":0.0006459709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04205622,"about_ca_topic_score_gemma":0.0484882,"domain_scores_codex":[0.9997196,0.00003223164,0.0000111346,0.00007641707,0.00009115162,0.00006950671],"domain_scores_gemma":[0.9993838,0.0002053951,0.0001435777,0.00008539837,0.0001272853,0.00005456309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004961801,0.0001563304,0.1825911,0.0001657968,0.0002429379,0.0009908797,0.0002980659,0.6166097,0.0439411,0.002365308,0.002409511,0.1497331],"study_design_scores_gemma":[0.0000115402,0.00006853784,0.02442867,0.00001362039,0.00002218893,0.0001108231,0.00006878195,0.9634483,0.01007192,0.001046725,0.000692994,0.00001588811],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9570576,0.0002944053,0.03868016,0.0001283337,0.00001158885,0.00003800302,0.0005119506,0.0009470849,0.002330804],"genre_scores_gemma":[0.9791753,0.0000680359,0.01914895,0.0000391332,0.000008006071,0.00000854255,0.000809805,0.00002359362,0.0007185155],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04205622,"threshold_uncertainty_score":0.08362287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2204055381856903,"score_gpt":0.256451019581437,"score_spread":0.03604548139574662,"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."}}