{"id":"W2017494972","doi":"10.4236/ti.2012.31002","title":"International Technology Mergers & Acquisitions and Raising the Competitiveness of China Equipment Manufacturing Industry","year":2012,"lang":"en","type":"article","venue":"Technology and Investment","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Social Science Fund of China","keywords":"China; Mergers and acquisitions; Business; Industrial organization; Raising (metalworking); Margin (machine learning); Capital (architecture); Manufacturing; International trade; Commerce; Finance; Marketing; Computer science; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001069466,0.0002480094,0.0001353178,0.001336937,0.001266943,0.003171611,0.0002369631,0.0005725092,0.002268719],"category_scores_gemma":[0.00176447,0.0001252489,0.0002666306,0.001537237,0.00115709,0.00191908,0.0008706628,0.000405771,0.00007786331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004347743,"about_ca_system_score_gemma":0.00806623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0295081,"about_ca_topic_score_gemma":0.02854916,"domain_scores_codex":[0.9991193,0.0001678679,0.00003457012,0.00007859801,0.0003010913,0.0002985628],"domain_scores_gemma":[0.9992384,0.0001177837,0.0003258539,0.00002289578,0.0001717754,0.000123247],"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":[0.0002146773,0.0001400109,0.2634952,0.0002837905,0.00007023544,0.002104992,0.002646181,0.01442252,0.006086462,0.5595644,0.01240082,0.1385707],"study_design_scores_gemma":[0.00006964289,0.0003581415,0.7234151,0.0003758323,0.0002677094,0.001243775,0.01092221,0.05120786,0.0131575,0.0929208,0.1059023,0.0001591965],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8916529,0.003605368,0.003711933,0.007408459,0.00008581746,0.00006924821,0.00008131949,0.00004363953,0.09334134],"genre_scores_gemma":[0.9955676,0.001133864,0.0004232065,0.00009913438,0.00003563311,0.000008123132,0.00002566885,0.000002481459,0.002704253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0295081,"threshold_uncertainty_score":0.05867267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0246467811641691,"score_gpt":0.2514895485050477,"score_spread":0.2268427673408786,"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."}}