{"id":"W6894221860","doi":"10.5683/sp3/ocsdtj","title":"[Untitled] Alberta. 1:50,000. Map Sheet 084H15, 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":[0.0005384322,0.002414772,0.001601234,0.006280652,0.001249479,0.004005361,0.002621331,0.000771516,0.1818275],"category_scores_gemma":[0.002531706,0.00111055,0.0006964314,0.02830215,0.0005228155,0.001230496,0.001116377,0.001524331,0.1788789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006930502,"about_ca_system_score_gemma":0.013767,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8330777,"about_ca_topic_score_gemma":0.8842849,"domain_scores_codex":[0.9993297,0.00003001957,0.00004384089,0.0001498195,0.0002972405,0.0001493995],"domain_scores_gemma":[0.9980364,0.0001303525,0.0001248789,0.0002422529,0.001256313,0.0002097468],"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":[0.00001615931,0.000003766956,0.0003192669,0.0001663068,0.000006646654,0.000008365882,0.00001755723,0.00009369473,0.00002996023,0.00017588,0.9964979,0.002664396],"study_design_scores_gemma":[0.000049891,0.000003341533,0.006839982,0.0001579749,0.0000106388,0.00002033915,0.0001146261,0.0001231747,0.0001222878,0.0003745899,0.9921616,0.00002159462],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005495619,0.00005366614,0.00003324493,0.00002164544,0.00002180765,0.000005145554,0.9971974,0.0001817414,0.002430322],"genre_scores_gemma":[0.000360107,0.0001034259,0.0002288664,0.00002158386,0.000005903642,0.00002487677,0.9945331,0.0001179171,0.004604255],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1818275,"threshold_uncertainty_score":0.6082735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01311564479775734,"score_gpt":0.2595232126985537,"score_spread":0.2464075679007964,"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."}}