{"id":"W6913037850","doi":"10.5683/sp3/g1gtvy","title":"Bamaji Lake Ontario. 1:50,000. Map Sheet 052O03, ed. 2, 1986","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; Government (linguistics)","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":[0.0003945408,0.001911158,0.001280371,0.005175851,0.001466291,0.002488856,0.001778237,0.0005302783,0.1085062],"category_scores_gemma":[0.002099381,0.0009963642,0.000569378,0.02308768,0.0004278108,0.0009964814,0.0009270867,0.0008614755,0.08094767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009607506,"about_ca_system_score_gemma":0.01543974,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9099188,"about_ca_topic_score_gemma":0.9515395,"domain_scores_codex":[0.9994206,0.00002392067,0.0000430887,0.0001273783,0.0002375257,0.0001474256],"domain_scores_gemma":[0.9984658,0.00007910799,0.0001664843,0.0001695246,0.0009435849,0.0001754231],"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":[0.0000196185,0.000004496216,0.001020519,0.0003224825,0.000009046472,0.00001350386,0.00004896156,0.00007899045,0.00004551414,0.0001957353,0.994329,0.003912094],"study_design_scores_gemma":[0.00003601448,0.000003527116,0.01917762,0.0001563123,0.00001226548,0.00002135785,0.0001628647,0.00009254847,0.0000952825,0.0001399813,0.9800869,0.00001525153],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001051229,0.00005991446,0.00002223435,0.00002307891,0.000009189644,0.000005971984,0.9979609,0.00007233082,0.001741201],"genre_scores_gemma":[0.0007281871,0.0001601196,0.0002009735,0.00001595435,0.000004944808,0.00004988947,0.9932833,0.00006228065,0.005494404],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1085062,"threshold_uncertainty_score":0.3629894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644532107057422,"score_gpt":0.2528834885970469,"score_spread":0.2364381675264727,"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."}}