{"id":"W6894370489","doi":"10.5683/sp3/3z5qlq","title":"[Untitled] Alberta. 1:50,000. Map Sheet 084H14, ed. 1, 1983","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); Digital mapping; Aerial photography; Geographic information system; Orthophoto; 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":[0.0005465575,0.002422653,0.001614366,0.006361752,0.001277086,0.004039654,0.002641748,0.000786349,0.1815338],"category_scores_gemma":[0.00257592,0.001128525,0.000699025,0.02843624,0.0005305404,0.001244408,0.001134572,0.001546462,0.17915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006951932,"about_ca_system_score_gemma":0.01377127,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8301194,"about_ca_topic_score_gemma":0.883067,"domain_scores_codex":[0.9993204,0.00003087229,0.00004441927,0.0001519721,0.000300256,0.0001520857],"domain_scores_gemma":[0.9980106,0.0001340168,0.0001268757,0.0002461009,0.001269396,0.000213063],"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":[0.00001608638,0.000003760074,0.0003181043,0.0001654078,0.000006627971,0.000008311381,0.00001757462,0.00009273798,0.00002957166,0.0001763633,0.9965592,0.002606257],"study_design_scores_gemma":[0.00005025939,0.000003351493,0.00677142,0.000157056,0.00001068887,0.00002022563,0.0001154223,0.0001222538,0.0001210144,0.0003788403,0.9922277,0.0000217488],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005451762,0.00005334006,0.00003273381,0.00002190041,0.00002164485,0.000005104334,0.9972463,0.0001796507,0.00238494],"genre_scores_gemma":[0.0003536314,0.0001034256,0.000227463,0.00002157888,0.000005938275,0.00002513746,0.9945846,0.0001172005,0.004561045],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1815338,"threshold_uncertainty_score":0.607291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01304080350475026,"score_gpt":0.259473102750763,"score_spread":0.2464322992460127,"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."}}