{"id":"W7017883126","doi":"","title":"Canada VMap1, Library 16: Hydrography Text Features","year":2016,"lang":"en","type":"other","venue":"The Faculty Digital Archive (New York University)","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scale (ratio); Vector map; Product (mathematics); Hydrography; Topographic map (neuroanatomy); National library; Geographic information system","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005804816,0.001244805,0.000829728,0.008589468,0.003252528,0.005025652,0.002345088,0.0005395154,0.4402432],"category_scores_gemma":[0.0047879,0.00075175,0.0005563197,0.02896591,0.0005930054,0.001949668,0.001581258,0.0007250599,0.2272183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01543693,"about_ca_system_score_gemma":0.05465569,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9358986,"about_ca_topic_score_gemma":0.9412129,"domain_scores_codex":[0.9989195,0.00003143904,0.00004705026,0.0001110044,0.0007296606,0.0001613959],"domain_scores_gemma":[0.9950016,0.0001861421,0.000141877,0.0002977451,0.003976625,0.0003959351],"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":[0.00002029943,0.00000638672,0.0004003121,0.0001296855,0.00000268604,0.00002325307,0.0000925165,0.0001445947,0.000101213,0.001045684,0.9766065,0.02142677],"study_design_scores_gemma":[0.000006561361,0.000002083367,0.002319217,0.00004739649,0.000003082418,0.00002020695,0.00009790195,0.0001481892,0.0002231984,0.0002953534,0.9968221,0.00001471447],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0009250374,0.0003012522,0.00151936,0.0003983696,0.0001624705,0.0001979374,0.733941,0.006079273,0.2564754],"genre_scores_gemma":[0.01029462,0.00124459,0.006853614,0.0002434874,0.0001144581,0.0003082359,0.5033902,0.007356372,0.4701945],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5597569,"threshold_uncertainty_score":0.7984252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01127362572962228,"score_gpt":0.2008696007672993,"score_spread":0.189595975037677,"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."}}