{"id":"W6931690553","doi":"10.5683/sp3/m510ci","title":"Cape Duncan Ontario. 1:50,000. Map Sheet 043A10, ed. 1, 1994","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Cape; Natural (archaeology); Raster graphics; Aerial photography; Digital mapping; Topographic map (neuroanatomy); Orthophoto","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002617101,0.0005638485,0.0005021613,0.00009307954,0.0001412382,0.0001372963,0.0008255871,0.0009030645,0.001056399],"category_scores_gemma":[0.0001835673,0.0005560471,0.0002676957,0.0000729653,0.0001128676,0.000004911241,0.0005992515,0.0007428335,0.00005643683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001093409,"about_ca_system_score_gemma":0.0006054395,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2866002,"about_ca_topic_score_gemma":0.4895552,"domain_scores_codex":[0.9977552,0.0001125585,0.0005672238,0.0006030331,0.0004141561,0.0005478688],"domain_scores_gemma":[0.9974385,0.00002236474,0.0003606548,0.001851105,0.0001038584,0.0002235559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003180145,0.00006156119,0.0001560864,0.0002115599,0.0001789695,0.00004269658,0.00003790517,0.00005109609,0.0001169546,0.000006106817,0.9988374,0.0002679264],"study_design_scores_gemma":[0.0003388026,0.0001703521,0.0003195661,0.00007770356,0.0001299786,0.00006836442,0.00002925591,0.00001408323,0.0002391435,0.000007503004,0.9979503,0.0006550041],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001042621,0.0007915469,0.00005038384,0.0001492009,0.0005843191,0.0002407107,0.9942871,0.0000271571,0.003765315],"genre_scores_gemma":[0.00001396644,0.0006814206,0.001096801,0.001473717,0.0009480555,0.00004492121,0.9926341,0.00005534671,0.00305167],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.202955,"threshold_uncertainty_score":0.9998568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006677434833356559,"score_gpt":0.2403633695492869,"score_spread":0.2336859347159303,"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."}}