{"id":"W6950466559","doi":"10.5683/sp3/8ywcrd","title":"McBride British Columbia. 1:50,000. Map Sheet 093H08, ed. 1, 1968","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Government (linguistics); Aerial photography; Orthophoto; Geographic information system","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":[0.0004315828,0.002048619,0.001533502,0.005891421,0.001696076,0.004180391,0.001942388,0.0007017202,0.2038091],"category_scores_gemma":[0.003296041,0.001082402,0.0005166356,0.02799332,0.0004815962,0.001484054,0.001148089,0.001485189,0.1801768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007562747,"about_ca_system_score_gemma":0.01390026,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8966501,"about_ca_topic_score_gemma":0.9348229,"domain_scores_codex":[0.9993462,0.0000309141,0.0000495521,0.0001757835,0.0002548152,0.0001427617],"domain_scores_gemma":[0.9977996,0.0001425739,0.0001352259,0.0002673075,0.001441498,0.0002137231],"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":[0.00001053579,0.000002153156,0.000265619,0.0001437771,0.000004894276,0.000008326301,0.0000162705,0.00003795971,0.00001868736,0.0001301346,0.9966397,0.002721905],"study_design_scores_gemma":[0.00001913499,0.000001788318,0.005802056,0.0002037402,0.000006859785,0.00001823599,0.00009982008,0.00006012775,0.0000757231,0.0001749763,0.9935217,0.00001570956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006702589,0.00008989289,0.00002360084,0.00003027089,0.0000171303,0.000004401405,0.9964844,0.0001357346,0.003147537],"genre_scores_gemma":[0.000663447,0.0002697756,0.0001898369,0.00003253319,0.000007431975,0.00004389281,0.9855168,0.0001703605,0.01310586],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2038091,"threshold_uncertainty_score":0.6818094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01150975209496717,"score_gpt":0.2418263926265521,"score_spread":0.2303166405315849,"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."}}