{"id":"W2598564337","doi":"10.1504/ijesd.2017.10004021","title":"Impact of seasonal temperature rise on labour and capital productivity in manufacturing sector: a study with Canadian panel data","year":2017,"lang":"en","type":"article","venue":"International Journal of Environment and Sustainable Development","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Productivity; Agricultural economics; Panel data; Production (economics); Climate change; Per capita; Manufacturing sector; Agriculture; Economics; Manufacturing; Population; Agricultural productivity; Natural resource economics; Environmental science; Business; Geography; Labour economics; Economic growth; Oceanography; Demography","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":[0.00141279,0.0004707643,0.0005675966,0.001404022,0.001899202,0.00131511,0.001395609,0.0004636766,0.002898602],"category_scores_gemma":[0.003030466,0.0003704302,0.001338403,0.0065433,0.0004867152,0.0004822138,0.0008178155,0.0009641664,0.0004363208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01853999,"about_ca_system_score_gemma":0.01587351,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9950487,"about_ca_topic_score_gemma":0.9961738,"domain_scores_codex":[0.9989216,0.00014574,0.00004984094,0.0001871471,0.0002709809,0.0004245991],"domain_scores_gemma":[0.9968253,0.0005050409,0.0005051276,0.0002688263,0.001415784,0.0004799563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001408509,0.00008028703,0.987556,0.00005394274,0.0003866518,0.0001373611,0.0005805328,0.002088237,0.000234588,0.0003206359,0.004262418,0.004158636],"study_design_scores_gemma":[0.000007338042,0.00001511641,0.9957165,0.0000160968,0.0000647174,0.00002092522,0.0006570424,0.001540361,0.00008017349,0.00002606509,0.001840359,0.00001541648],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9740801,0.0008671445,0.0002630876,0.0004583506,0.00001875139,0.00003241025,0.02166943,0.00002304368,0.002587857],"genre_scores_gemma":[0.9821451,0.0005248787,0.0002454368,0.00009245758,0.000006182374,0.00002213033,0.01550017,0.000009746602,0.001453891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01853999,"threshold_uncertainty_score":0.1345177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04514598900708355,"score_gpt":0.2502483403653484,"score_spread":0.2051023513582648,"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."}}