{"id":"W2154958897","doi":"10.1109/mts.2009.933028","title":"K-Net and Canadian Aboriginal communities","year":2009,"lang":"en","type":"article","venue":"IEEE Technology and Society Magazine","topic":"E-Government and Public Services","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Square (algebra); Net (polyhedron); Mile; Geography; Population; Net migration rate; Last mile (transportation); Agricultural economics; Socioeconomics; Economic growth; Telecommunications; Engineering; Demography; Sociology; Population growth; Economics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001272717,0.000373236,0.0003164025,0.002250531,0.01696632,0.003447186,0.001169861,0.0008396035,0.01397942],"category_scores_gemma":[0.004148371,0.000236608,0.0004403281,0.003669037,0.002662731,0.001128567,0.003743782,0.001440464,0.0008543037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05579353,"about_ca_system_score_gemma":0.09504236,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9967021,"about_ca_topic_score_gemma":0.9981346,"domain_scores_codex":[0.9980335,0.0001900825,0.00004085722,0.000139715,0.0006066054,0.000989256],"domain_scores_gemma":[0.9958896,0.0002102599,0.0002431939,0.00008936507,0.001592755,0.001974963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004338628,0.000245486,0.419128,0.0006669037,0.0001237947,0.002927546,0.1527252,0.000495067,0.0008137934,0.07475102,0.1465927,0.2010965],"study_design_scores_gemma":[0.00004716999,0.00008995536,0.4314855,0.001138584,0.0001371127,0.00111231,0.1804693,0.0005118194,0.0002553261,0.003983341,0.3806281,0.0001413842],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5778773,0.01419397,0.0004846017,0.04309768,0.0005909284,0.0002085563,0.004196535,0.00007533145,0.359275],"genre_scores_gemma":[0.9155516,0.01067868,0.0007411806,0.003950255,0.0001155496,0.00009662048,0.0009870996,0.00004529003,0.06783369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05579353,"threshold_uncertainty_score":0.4048122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009656362459982006,"score_gpt":0.2714410050446073,"score_spread":0.2617846425846252,"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."}}