{"id":"W6959397842","doi":"10.11575/sppp.v5i0.42393.g30288","title":"The Big and the Small of Tax Support for R&D in Canada","year":2017,"lang":"en","type":"article","venue":"University of Calgary","topic":"Fern and Epiphyte Biology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subsidy; Tax credit; Tax reform; Government (linguistics); Indirect tax; Tax policy; Capital (architecture); Value-added tax","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002788243,0.0003522307,0.0006171992,0.004291785,0.009368012,0.008665444,0.001766809,0.001251267,0.004963667],"category_scores_gemma":[0.01146036,0.0006488523,0.0007446326,0.008939886,0.003257473,0.001493038,0.001967374,0.002514038,0.0003705966],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.3140739,"about_ca_system_score_gemma":0.4413698,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9988877,"about_ca_topic_score_gemma":0.9993388,"domain_scores_codex":[0.9907512,0.0003334092,0.0002763166,0.0005789315,0.004251608,0.003808482],"domain_scores_gemma":[0.9847828,0.001748568,0.001244011,0.0004773654,0.006776449,0.004970871],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009951154,0.0002291724,0.2540142,0.001072433,0.0003077592,0.001207905,0.00799343,0.009286491,0.00431574,0.2715082,0.1650878,0.2839818],"study_design_scores_gemma":[0.0001017047,0.0001045219,0.6188038,0.0006132546,0.0001837009,0.0002052933,0.008606927,0.004268998,0.002208676,0.005424608,0.3592257,0.0002528052],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5871974,0.02539906,0.002631198,0.141317,0.0008156666,0.0003993933,0.01499578,0.0004984189,0.2267461],"genre_scores_gemma":[0.9300728,0.009946765,0.002109904,0.006212503,0.00008865984,0.00004693902,0.002205096,0.00009001206,0.04922716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9972118,"threshold_uncertainty_score":0.795577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02295802377953641,"score_gpt":0.1746760888156963,"score_spread":0.1517180650361599,"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."}}