{"id":"W2934763937","doi":"10.1038/s41598-019-41959-8","title":"Intense bone fluorescence reveals hidden patterns in pumpkin toadlets","year":2019,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Muséum National d'Histoire Naturelle; Université Paris-Saclay; Université Paris Diderot; Universidade Estadual de Campinas; Université d'Orléans; Centre National de la Recherche Scientifique; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Université de Versailles Saint-Quentin-en-Yvelines; Fundação de Amparo à Pesquisa do Estado de São Paulo; York University; Université de Lausanne; New York University Abu Dhabi","keywords":"Fluorescence; Computer science; Physics; Optics","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":[],"consensus_categories":[],"category_scores_codex":[0.001319411,0.0001388315,0.0001587986,0.0001777499,0.0000888623,0.0001820175,0.00021103,0.0001177285,0.0002807823],"category_scores_gemma":[0.0002018889,0.0001384977,0.00008701863,0.0003154912,0.0001125593,0.00001388883,0.0001150802,0.0001001332,0.000323277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002476708,"about_ca_system_score_gemma":0.00009048105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002641253,"about_ca_topic_score_gemma":0.00004474076,"domain_scores_codex":[0.9977486,0.00007373004,0.0006014957,0.0009596981,0.0003249069,0.0002916309],"domain_scores_gemma":[0.9979739,0.000008445812,0.0002797619,0.001416563,0.0002312151,0.00009014408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000113763,0.00005145102,0.08572006,0.0000250111,0.000006437823,0.00003376421,0.0001205259,0.00000488325,0.8999252,0.000114613,0.01317766,0.000808974],"study_design_scores_gemma":[0.0003452511,0.0000587298,0.1213489,0.00009981231,0.000009087289,0.0002958538,0.0003259205,0.00006450048,0.7969462,0.0006546907,0.07940812,0.0004429459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931758,0.0001163662,0.00007957855,0.0002987481,0.004716989,0.0003874275,0.000005480846,0.00002228215,0.001197334],"genre_scores_gemma":[0.9744356,0.00001495751,0.0001833206,0.00009869679,0.00006304155,0.00003404568,0.0003145628,0.00001750376,0.02483822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1029791,"threshold_uncertainty_score":0.5647771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283625094240211,"score_gpt":0.2567716482478078,"score_spread":0.2439353973054057,"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."}}