{"id":"W6987312261","doi":"","title":"SR&amp;amp;ED Program Refunds For Canadian Businesses Through Evamax.com Helps Maximize Tax Credits","year":2011,"lang":"en","type":"other","venue":"","topic":"Canadian Policy and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Tax credit; Work (physics); Tax incentive; Production (economics)","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":[0.001090508,0.0004343052,0.0003119685,0.002269374,0.008850265,0.004476675,0.001065534,0.001825285,0.2366271],"category_scores_gemma":[0.00500553,0.0003936341,0.0005096037,0.002743999,0.001033448,0.001538326,0.001713353,0.001693464,0.02315911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03040481,"about_ca_system_score_gemma":0.1156249,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9590274,"about_ca_topic_score_gemma":0.9894767,"domain_scores_codex":[0.9981723,0.00008053542,0.00002478834,0.00008060026,0.0009683145,0.0006734132],"domain_scores_gemma":[0.9955088,0.0002184365,0.00008112468,0.0001664926,0.002971395,0.001053774],"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":[0.00002040731,0.00003873976,0.001878925,0.00004063483,0.00000438682,0.00004224008,0.0002572566,0.0001442557,0.0001860315,0.02124868,0.934845,0.04129348],"study_design_scores_gemma":[0.00001276917,0.00001061049,0.005262133,0.00005847092,0.000008412705,0.00003204554,0.0006354663,0.0003355927,0.0003628906,0.001165289,0.9920994,0.00001702281],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.005521228,0.0005468662,0.0007210101,0.02163464,0.0003089254,0.0001568305,0.006282013,0.0007408963,0.9640876],"genre_scores_gemma":[0.03793634,0.000869316,0.001527777,0.002151547,0.000053122,0.00003343899,0.001956026,0.0002577736,0.9552146],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2366271,"threshold_uncertainty_score":0.7915965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07936041006866497,"score_gpt":0.3399430573874027,"score_spread":0.2605826473187378,"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."}}