{"id":"W2951690425","doi":"10.1371/journal.pone.0205043","title":"Can Drosophila melanogaster tell who’s who?","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Neurobiology and Insect Physiology Research","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Guelph; University of Toronto; Canadian Institute for Advanced Research","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canadian Institute for Advanced Research","keywords":"Drosophila melanogaster; Melanogaster; Drosophila (subgenus); Convolutional neural network; Biology; Set (abstract data type); Feature (linguistics); Similarity (geometry); Computer science; ENCODE; Artificial intelligence; Evolutionary biology; Cognitive science; Communication; Psychology; Genetics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001045961,0.0003452256,0.0003656317,0.0004369238,0.0008393739,0.00168061,0.0003996738,0.001264651,0.005504078],"category_scores_gemma":[0.00520628,0.0002176125,0.0002778804,0.0004255339,0.001800628,0.002882498,0.0005938171,0.001543408,0.002732271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008998446,"about_ca_system_score_gemma":0.0005819072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004989044,"about_ca_topic_score_gemma":0.006377726,"domain_scores_codex":[0.999666,0.0001286306,0.00001294618,0.00008582898,0.00007462464,0.00003199008],"domain_scores_gemma":[0.9989598,0.0005718101,0.0001070874,0.00009860547,0.0001582901,0.0001045191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006929343,0.0000740678,0.0218474,0.001427379,0.0002068474,0.001108392,0.005072554,0.001457308,0.02015685,0.2178896,0.4562742,0.2737924],"study_design_scores_gemma":[0.00006860067,0.00008266917,0.01328563,0.0007146994,0.0001083768,0.0008861654,0.00478194,0.001703772,0.006782958,0.1650391,0.8064318,0.0001143725],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.132379,0.107975,0.01754394,0.5480101,0.01504826,0.00009263163,0.005516137,0.001021545,0.1724134],"genre_scores_gemma":[0.8073522,0.0580103,0.0176182,0.06596033,0.001840466,0.00008237476,0.002551946,0.0002437297,0.04634044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005504078,"threshold_uncertainty_score":0.01841295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09043920021211303,"score_gpt":0.280057370404925,"score_spread":0.1896181701928119,"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."}}