{"id":"W6950614509","doi":"10.5683/sp3/bveowp","title":"Algonquin (West) Ontario. 1:50,000. Map Sheet 031E10, ed. 2, 1960","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; Viewshed analysis; Raster graphics; Natural (archaeology); Aerial photography; Geographic information system; 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":[0.000474334,0.001874411,0.001299556,0.00429108,0.001367885,0.003446836,0.001698473,0.0005866021,0.177404],"category_scores_gemma":[0.002697845,0.0009501006,0.0006938378,0.01670553,0.0004852714,0.001330637,0.001183038,0.0009554815,0.1460649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007325139,"about_ca_system_score_gemma":0.01199317,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.868944,"about_ca_topic_score_gemma":0.9275936,"domain_scores_codex":[0.9993249,0.00003112234,0.0000487176,0.0001868666,0.0002550262,0.0001533026],"domain_scores_gemma":[0.9982369,0.0001202918,0.0001517549,0.000234212,0.001051066,0.0002057238],"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":[0.00001616518,0.000002319191,0.0005488914,0.000263815,0.000007589817,0.00001098589,0.00003660033,0.00005564727,0.00004022203,0.0002348417,0.9958325,0.002950388],"study_design_scores_gemma":[0.00002368121,0.000002122939,0.006403588,0.0001324294,0.000006671196,0.00001733989,0.00008769085,0.00005026843,0.00006054227,0.0001797214,0.9930245,0.00001141122],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006396316,0.00008637583,0.00003094812,0.00002881028,0.00001485159,0.000004931613,0.9970655,0.0001514014,0.002553377],"genre_scores_gemma":[0.0006380006,0.0002061355,0.0002474994,0.00002869975,0.000007787919,0.00003726583,0.9911698,0.0001932635,0.007471398],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.177404,"threshold_uncertainty_score":0.5934756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01920343145679068,"score_gpt":0.2638010568731686,"score_spread":0.2445976254163779,"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."}}