{"id":"W2951782131","doi":"","title":"An Overview of the World Agricultural Machinery Manufacturing Sector","year":2018,"lang":"tr","type":"article","venue":"DergiPark (Istanbul University)","topic":"Agricultural Engineering and Mechanization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Arable land; Diversification (marketing strategy); Agricultural economics; Agricultural machinery; Business; Agricultural productivity; Product (mathematics); Population; Production (economics); International trade; Economics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000795684,0.0003555228,0.0003088253,0.0002049992,0.0002552382,0.00005323886,0.000611426,0.0001500311,0.0003088157],"category_scores_gemma":[0.00001177478,0.0002598017,0.0001873866,0.001197286,0.000085086,0.0004203166,0.000131943,0.0003027245,0.00003664226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003209165,"about_ca_system_score_gemma":0.00002594894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001044037,"about_ca_topic_score_gemma":0.00051248,"domain_scores_codex":[0.9986951,0.0000683728,0.0002497948,0.0003287335,0.000264689,0.0003933179],"domain_scores_gemma":[0.999118,0.00003742955,0.0001381031,0.0003876456,0.0001594205,0.0001593948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004376083,0.0008698207,0.002669201,0.004700718,0.002387354,0.0001882814,0.01144383,0.256128,0.3210866,0.3499096,0.04433968,0.005839366],"study_design_scores_gemma":[0.002265085,0.0004090252,0.1665568,0.001783863,0.0009917441,0.00007258506,0.004497321,0.0247094,0.3780109,0.0001921384,0.4179798,0.0025314],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9727137,0.0005207914,0.001241748,0.0001215378,0.00162639,0.0003082617,0.0000811034,0.0003376042,0.02304883],"genre_scores_gemma":[0.9863259,0.0001770432,0.000454507,0.00002054712,0.0004655944,4.443804e-7,0.00003050691,0.00003445079,0.01249106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3736401,"threshold_uncertainty_score":0.9999854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453293243001638,"score_gpt":0.1954566841115104,"score_spread":0.180923751681494,"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."}}