{"id":"W6931553911","doi":"10.5683/sp3/frwmzu","title":"Kananaskis Lakes Alberta / British Columbia. 1:50,000. Map Sheet 082J11, ed. 1, 1967","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Geographic information system; Natural (archaeology); Aerial photography; Government (linguistics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003698607,0.002265219,0.001317738,0.005550038,0.001495188,0.00347878,0.001803097,0.0006165037,0.087592],"category_scores_gemma":[0.002079972,0.0007771475,0.0004881758,0.02467664,0.0004802338,0.0008567492,0.0007995352,0.001322985,0.0922558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008016436,"about_ca_system_score_gemma":0.0164298,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9221479,"about_ca_topic_score_gemma":0.9600952,"domain_scores_codex":[0.9994759,0.00001975437,0.00003472973,0.000123054,0.0002236872,0.0001230268],"domain_scores_gemma":[0.9984331,0.00008818327,0.00008557496,0.0001547461,0.001059974,0.0001784638],"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":[0.00001830677,0.000004380295,0.000597222,0.0001647568,0.000005913541,0.00001552529,0.0000186084,0.00009227172,0.00002913369,0.0001739346,0.9951288,0.003751148],"study_design_scores_gemma":[0.00003649517,0.000003088805,0.009594725,0.000188045,0.00001015376,0.00003105943,0.0001831492,0.0001533521,0.0001253399,0.0002689976,0.9893879,0.00001770818],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001508212,0.0001263379,0.00003161706,0.00003604396,0.00002226268,0.000006350165,0.9965821,0.0001417326,0.002902793],"genre_scores_gemma":[0.0007054963,0.0002652862,0.0001868014,0.00002154047,0.000005819376,0.00002496776,0.9916221,0.00006807834,0.007099906],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.087592,"threshold_uncertainty_score":0.2930245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009547603913462393,"score_gpt":0.2303851229573903,"score_spread":0.2208375190439279,"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."}}