{"id":"W6999090439","doi":"","title":"Canada VMap1, Library 16: Data Quality Lines","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); Product (mathematics); Vector map; Geographic information system; Data quality; Quality (philosophy); Base (topology); 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":[],"category_scores_codex":[0.007062077,0.001646865,0.001329436,0.01232817,0.004942964,0.01093556,0.004660298,0.001055812,0.3485241],"category_scores_gemma":[0.04919388,0.001798013,0.0008508555,0.03057303,0.001133209,0.004804257,0.002872785,0.001721478,0.2672831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03113294,"about_ca_system_score_gemma":0.1218352,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.900693,"about_ca_topic_score_gemma":0.8586449,"domain_scores_codex":[0.9876776,0.0007056079,0.001066282,0.001215224,0.008174662,0.001160575],"domain_scores_gemma":[0.9178176,0.002775863,0.001323696,0.006435897,0.06932735,0.002319577],"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":[0.00005887708,0.00001775383,0.0006336206,0.0001383244,0.000005807873,0.00001734071,0.0001405747,0.0001725017,0.0001842561,0.002299526,0.9737529,0.02257846],"study_design_scores_gemma":[0.00002527284,0.000004841444,0.003065814,0.0001128785,0.000006863986,0.00001569731,0.0001268082,0.0003273071,0.0006487806,0.0006043362,0.9950257,0.00003568194],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.001073183,0.000138606,0.007143477,0.00074765,0.0001918738,0.0009373385,0.8398336,0.01508353,0.1348508],"genre_scores_gemma":[0.007153726,0.0004154159,0.0254323,0.0004400384,0.00007219445,0.00162595,0.7925094,0.01227384,0.1600771],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3485241,"threshold_uncertainty_score":0.9292513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0641679987389519,"score_gpt":0.2736871458552839,"score_spread":0.209519147116332,"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."}}