{"id":"W6969281763","doi":"10.5683/sp3/egcmfx","title":"Wigwasan Lake (West) Ontario. 1:50,000. Map Sheet 052I03, ed. 1, 1959","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; Natural (archaeology); Aerial photography; Topographic map (neuroanatomy); Viewshed analysis; Digital mapping","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":[0.0003984523,0.001700278,0.00111986,0.004417525,0.001612845,0.002695151,0.001698193,0.0005378582,0.119845],"category_scores_gemma":[0.00241296,0.0008914358,0.0006249389,0.0181788,0.0005378844,0.001118789,0.001093463,0.0009051632,0.08772379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008861466,"about_ca_system_score_gemma":0.01615161,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9073167,"about_ca_topic_score_gemma":0.9568754,"domain_scores_codex":[0.9994285,0.00002485208,0.00004541584,0.0001372392,0.0002214497,0.0001425906],"domain_scores_gemma":[0.9983885,0.00008812763,0.0001594614,0.0001960062,0.0009974665,0.0001703627],"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":[0.00002039648,0.000003549167,0.001024668,0.0003071018,0.00001009403,0.00001564146,0.00005922724,0.00006257254,0.00004762139,0.000270535,0.995076,0.003102725],"study_design_scores_gemma":[0.00003314005,0.000002812879,0.01196755,0.0001410289,0.000009505917,0.00002031496,0.0001504894,0.00005623378,0.00008141825,0.0001544453,0.9873696,0.00001350797],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009947389,0.00006252923,0.00002509909,0.00002915618,0.00001303176,0.000006046492,0.997456,0.00008735947,0.002221227],"genre_scores_gemma":[0.0009161088,0.000163339,0.0002096751,0.00002330561,0.00000636393,0.00004354447,0.9922315,0.00009814704,0.006307947],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.119845,"threshold_uncertainty_score":0.4009214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843224902142998,"score_gpt":0.258847230069074,"score_spread":0.240414981047644,"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."}}