{"id":"W6931719080","doi":"10.5683/sp3/fdmeum","title":"Lake Joseph Ontario. 1:50,000. Map Sheet 031E04, ed. 4, 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; Natural (archaeology); Raster graphics; Aerial photography; Topographic map (neuroanatomy); Geographic information system; 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":[0.0003942863,0.001964845,0.001343497,0.005050141,0.001501654,0.002805822,0.001704899,0.0005775561,0.1468161],"category_scores_gemma":[0.002145255,0.001054205,0.0005834364,0.02151209,0.0004651177,0.001132593,0.001029731,0.0008817305,0.1211942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009075684,"about_ca_system_score_gemma":0.01348487,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.862633,"about_ca_topic_score_gemma":0.9226276,"domain_scores_codex":[0.9993739,0.00002444059,0.00004644577,0.0001373777,0.0002710992,0.0001467904],"domain_scores_gemma":[0.9984117,0.00009463494,0.0001749275,0.0001783916,0.0009589527,0.0001813836],"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":[0.00001605854,0.000004017671,0.0007515858,0.0002462292,0.000007242401,0.00001069237,0.00003427582,0.00006912485,0.00003780122,0.0001678726,0.9955851,0.003069928],"study_design_scores_gemma":[0.00003764202,0.00000311686,0.01345532,0.0001258837,0.000009970034,0.00001784043,0.0001130282,0.00009408723,0.00009894234,0.000144466,0.9858849,0.0000147955],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007463293,0.00004277605,0.00002108995,0.00002154545,0.000007561649,0.000004850081,0.9980775,0.00008408622,0.001665975],"genre_scores_gemma":[0.0004748352,0.0001300779,0.0001613987,0.00001591445,0.000004511098,0.00004014352,0.9932324,0.00006585909,0.005874907],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1468161,"threshold_uncertainty_score":0.4911487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715268613420881,"score_gpt":0.2553730084046718,"score_spread":0.2382203222704629,"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."}}