{"id":"W6894313257","doi":"10.5683/sp3/xlknno","title":"Cartier Ontario. 1:50,000. Map Sheet 041I12, ed. 2, 1978","year":2021,"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; Aerial photography; Natural (archaeology); Digital mapping; Government (linguistics); Viewshed analysis","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":[0.000401135,0.001925387,0.001376948,0.005626523,0.001412804,0.003089624,0.00159653,0.0005750018,0.2215523],"category_scores_gemma":[0.002542613,0.001013827,0.0006811231,0.02099957,0.0004480687,0.001304283,0.0009935678,0.0009507273,0.1836744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007521266,"about_ca_system_score_gemma":0.01150344,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8495813,"about_ca_topic_score_gemma":0.910906,"domain_scores_codex":[0.9993683,0.0000271564,0.00004615682,0.0001442317,0.0002699421,0.0001443559],"domain_scores_gemma":[0.9981523,0.0001237021,0.0001616059,0.0002201245,0.001152332,0.0001900087],"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":[0.00001188518,0.000002216946,0.0003795025,0.0001700001,0.000005136765,0.000008055435,0.00002235576,0.00004034545,0.00002434986,0.0001588114,0.9964166,0.00276076],"study_design_scores_gemma":[0.00002328688,0.000001979282,0.005951562,0.0001134322,0.000006558794,0.00001479296,0.00007834611,0.00004837118,0.00006077651,0.0001216558,0.9935684,0.00001103401],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005365051,0.00005836597,0.00002538557,0.00002511707,0.00001245259,0.000005247405,0.9969861,0.0001241362,0.002709516],"genre_scores_gemma":[0.0005034537,0.0001860392,0.0002277662,0.00002012559,0.000007840286,0.000039946,0.9894814,0.0001438641,0.009389468],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2215523,"threshold_uncertainty_score":0.7411664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01716797783319842,"score_gpt":0.2547026003824496,"score_spread":0.2375346225492512,"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."}}