{"id":"W6980431285","doi":"","title":"Canada VMap1, Library 16: Data Quality Void Collection Area","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; Geographic information system; Data quality; Product (mathematics); Data collection; Digital data; Base (topology)","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":[0.005064125,0.001427762,0.001506724,0.01365898,0.005471583,0.008244601,0.004125328,0.0007751483,0.2840061],"category_scores_gemma":[0.03539814,0.001348649,0.0009013394,0.03415393,0.001090892,0.003366965,0.002952016,0.001393406,0.1487277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03821829,"about_ca_system_score_gemma":0.1747152,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9582239,"about_ca_topic_score_gemma":0.9473651,"domain_scores_codex":[0.9898133,0.0005167307,0.0008387754,0.00102236,0.006691084,0.00111763],"domain_scores_gemma":[0.9413002,0.001876767,0.001301195,0.003628151,0.04966078,0.002232926],"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":[0.000056324,0.00001729017,0.001167448,0.0001991473,0.000007204324,0.00001409547,0.0001431531,0.0001560435,0.0001246029,0.001998621,0.9769121,0.01920401],"study_design_scores_gemma":[0.00002656725,0.000005392526,0.007775482,0.0001511552,0.00001006048,0.00001331222,0.0002160095,0.0002996382,0.0005445328,0.0004520285,0.9904646,0.00004139317],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008830685,0.0001074567,0.002609581,0.0004162578,0.0001204662,0.0008648417,0.9298263,0.003403127,0.06176898],"genre_scores_gemma":[0.008336697,0.0004306567,0.01496965,0.000354319,0.00005464752,0.002504926,0.8674731,0.004245973,0.10163],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2840061,"threshold_uncertainty_score":0.9500949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06113948547245458,"score_gpt":0.2598315722908753,"score_spread":0.1986920868184207,"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."}}