{"id":"W3098846161","doi":"","title":"Simple Subroutine for Inhomogeneous Deployment","year":2014,"lang":"en","type":"preprint","venue":"SOURCE Sheridan's Institutional Repository (Sheridan College)","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Software deployment; Computer science; Simple (philosophy); Subroutine; Cluster analysis; Inference; Distributed computing; Wireless network; Wireless; Algorithm; Artificial intelligence; Programming language; Telecommunications; Software engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005236434,0.001022827,0.0009676883,0.0002930135,0.0009754996,0.0002807436,0.0007751752,0.0007742733,0.00007331117],"category_scores_gemma":[0.0001159463,0.001134339,0.0006916143,0.0002141263,0.0002746276,0.0001508456,0.0004977924,0.0009440358,0.00004564112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122319,"about_ca_system_score_gemma":0.0006450852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008419632,"about_ca_topic_score_gemma":0.00003474433,"domain_scores_codex":[0.9956223,0.0001322614,0.001383742,0.001157273,0.0008275452,0.0008768372],"domain_scores_gemma":[0.9975556,0.0001504293,0.0003166466,0.001091685,0.0004336327,0.0004519804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001554349,0.0001132657,0.0002043243,0.001163599,0.0004817531,0.00007788129,0.0002311936,0.9656031,0.02330031,0.0005643726,0.005721813,0.002382978],"study_design_scores_gemma":[0.001742674,0.0001897594,0.0001646167,0.0004889123,0.0003050903,0.0003372623,0.0001332082,0.6539685,0.07260536,0.001382085,0.2665886,0.002093923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3883939,0.001089017,0.5931166,0.00007223378,0.004528896,0.001784222,0.0003410175,0.001279013,0.009395087],"genre_scores_gemma":[0.9850205,0.00006616575,0.007881237,0.0002642357,0.00233677,0.001024925,0.0004940983,0.0002796675,0.002632458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5966265,"threshold_uncertainty_score":0.9991106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01451019806284535,"score_gpt":0.2208868154455066,"score_spread":0.2063766173826613,"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."}}