{"id":"W2963098896","doi":"","title":"Approximation Schemes for Covering and Packing in the Streaming Model.","year":2017,"lang":"en","type":"article","venue":"Canadian Conference on Computational Geometry","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Streaming data; Approximation algorithm; Packing problems; Context (archaeology); Streaming algorithm; Theoretical computer science; Cluster analysis; Cover (algebra); Mathematical optimization; Algorithm; Mathematics; Upper and lower bounds; Artificial intelligence; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.002316551,0.001240753,0.001264898,0.001384888,0.001022911,0.002199595,0.003970414,0.001918726,0.006290164],"category_scores_gemma":[0.01116448,0.0005928975,0.001722534,0.002693841,0.001447398,0.007281021,0.003716117,0.003028735,0.001653403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00267914,"about_ca_system_score_gemma":0.001334883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002118347,"about_ca_topic_score_gemma":0.002465564,"domain_scores_codex":[0.9983824,0.0004000115,0.00009285183,0.0003147098,0.0005738673,0.0002360694],"domain_scores_gemma":[0.9949481,0.002418382,0.0003549625,0.001697506,0.0003283211,0.000252698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000358402,0.0002638579,0.001435615,0.0004421019,0.0000857319,0.0001595866,0.000426542,0.2062699,0.005251622,0.574867,0.01639393,0.1940457],"study_design_scores_gemma":[0.0000351694,0.00008936275,0.0002305167,0.00004496369,0.00003157234,0.0002648633,0.0000956963,0.7038025,0.00196177,0.2845549,0.008869127,0.00001955123],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01879259,0.001306044,0.9693002,0.0005532222,0.000136089,0.0001531262,0.0002848026,0.0005760633,0.008897856],"genre_scores_gemma":[0.4196739,0.002685744,0.5648494,0.0005326857,0.0003864044,0.0004657246,0.001343588,0.0003593499,0.009703211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006290164,"threshold_uncertainty_score":0.0210427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06221067361608096,"score_gpt":0.2934311865652908,"score_spread":0.2312205129492098,"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."}}