{"id":"W6931338264","doi":"10.5281/zenodo.5683922","title":"Nomada tiftonensis Cockerell","year":2010,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"DNA barcoding; Type (biology); Labrum; Taxonomy (biology); Adult male; Genetic divergence","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":[0.00006161464,0.0004214927,0.0001718923,0.00107882,0.002090435,0.0003165522,0.0004378938,0.0003952062,0.02250511],"category_scores_gemma":[0.0001721365,0.0001496709,0.000126991,0.0004292435,0.0004196605,0.000728956,0.0005415492,0.0004569697,0.005308677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006665575,"about_ca_system_score_gemma":0.0004805065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03956307,"about_ca_topic_score_gemma":0.1389435,"domain_scores_codex":[0.9999228,0.00000546217,0.000004747802,0.00002869458,0.00002407057,0.00001427266],"domain_scores_gemma":[0.9999185,0.000009599035,0.00001903984,0.0000119585,0.00002951857,0.0000113421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004011319,0.0001340705,0.09355767,0.001041692,0.00005624899,0.007430619,0.005596343,0.0005347118,0.02890182,0.01165993,0.190136,0.6605496],"study_design_scores_gemma":[0.00004473561,0.00009719325,0.2681721,0.0005471489,0.00004940065,0.005468362,0.003509643,0.0001776316,0.001658125,0.001805193,0.7184443,0.00002619396],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2130264,0.008572671,0.002749531,0.001236968,0.001223194,0.000338087,0.003969332,0.0005487333,0.768335],"genre_scores_gemma":[0.8841984,0.003711407,0.003262993,0.0008545537,0.0001993459,0.00009745549,0.003268797,0.0000427232,0.1043643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03956307,"threshold_uncertainty_score":0.07866555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02178659579073202,"score_gpt":0.2294695913462022,"score_spread":0.2076829955554702,"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."}}