{"id":"W6969496907","doi":"10.5683/sp3/kaddcy","title":"Low Water Lake Ontario. 1:50,000. Map Sheet 041P04, ed. 1, 1968","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; Aerial photography; Natural (archaeology); Raster graphics; Topographic map (neuroanatomy); Geographic information system; Orthophoto; Natural resource","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":[0.0004203031,0.001832983,0.001245954,0.005064958,0.001700623,0.002714677,0.0017543,0.0006212845,0.1253304],"category_scores_gemma":[0.002666034,0.0009920043,0.0006665965,0.0203104,0.0005462723,0.001246979,0.001229772,0.0009354157,0.09363631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009000821,"about_ca_system_score_gemma":0.01498657,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8799591,"about_ca_topic_score_gemma":0.939333,"domain_scores_codex":[0.9993285,0.00002907551,0.00005431969,0.0001577839,0.0002643577,0.0001659871],"domain_scores_gemma":[0.9982558,0.0001050445,0.0001843337,0.0002064065,0.001062634,0.0001857333],"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":[0.00001632987,0.000003352581,0.0008813291,0.0003011693,0.000008524837,0.00001320457,0.00004858308,0.00005206253,0.00004150976,0.0001887,0.996026,0.002419187],"study_design_scores_gemma":[0.0000349842,0.000003028758,0.01288918,0.00015944,0.00001029083,0.00002126356,0.0001367331,0.00006042901,0.00007633962,0.0001595321,0.986434,0.000014857],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007893367,0.00004875658,0.00001760536,0.00002706181,0.00001118524,0.000004829807,0.9983045,0.00006423348,0.001442894],"genre_scores_gemma":[0.0006359886,0.0001396482,0.0001593433,0.00002147229,0.000006624502,0.0000452334,0.9941027,0.00007486252,0.004814148],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1253304,"threshold_uncertainty_score":0.4192721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01284665687736144,"score_gpt":0.2381446707193238,"score_spread":0.2252980138419623,"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."}}