{"id":"W2159343862","doi":"10.5539/ijef.v4n8p126","title":"The Measurement of Ocean Scientific and Technological Progress Contribution in China","year":2012,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Office for Philosophy and Social Sciences","keywords":"Frontier; Technological change; China; Function (biology); Economics; Perspective (graphical); Technical progress; Marine technology; Scientific progress; Economy; Regional science; Oceanography; Geography; Macroeconomics; Computer science; Geology; Biology","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.001548368,0.000344149,0.0002515943,0.002359203,0.0003453071,0.0006524256,0.0004134327,0.0001892123,0.000471217],"category_scores_gemma":[0.002838686,0.0001427299,0.0003504541,0.003413465,0.0003643871,0.0007354246,0.0008352411,0.0002492285,0.0001055589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001615413,"about_ca_system_score_gemma":0.002271526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05302038,"about_ca_topic_score_gemma":0.04385559,"domain_scores_codex":[0.9992354,0.0001078856,0.00009247119,0.0001465363,0.0003140608,0.0001036916],"domain_scores_gemma":[0.9983147,0.0002901026,0.0006083901,0.0001385021,0.0004596271,0.0001886542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005188652,0.00002669629,0.9699636,0.00005972358,0.00007131649,0.0001876545,0.0006174972,0.005830024,0.001475061,0.001581697,0.0004772636,0.01965765],"study_design_scores_gemma":[0.000002297447,0.00002825023,0.9929687,0.000006875439,0.00002510182,0.00002866767,0.0001867337,0.004956831,0.0006209825,0.0001626354,0.001005973,0.000006793232],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966787,0.00009767524,0.0004801032,0.00008730189,0.000005808572,0.000009069769,0.0005176751,0.00001189929,0.00211179],"genre_scores_gemma":[0.998556,0.0001238768,0.0002276656,0.000006027169,0.000004660164,0.00000765629,0.0004652646,0.000001759938,0.0006072269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9976408,"threshold_uncertainty_score":0.1054236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02406734853522356,"score_gpt":0.2161404880269558,"score_spread":0.1920731394917323,"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."}}