{"id":"W2382594364","doi":"","title":"Status and Trends of Biomass Energy Based on Bibliometrics","year":2014,"lang":"en","type":"article","venue":"Anhui nongye kexue","topic":"Power Systems and Renewable Energy","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biomass (ecology); Bibliometrics; China; Subject (documents); Index (typography); Web of science; Energy (signal processing); Environmental science; Environmental resource management; Agricultural engineering; Computer science; Geography; Ecology; Political science; Library science; Mathematics; Biology; Statistics; World Wide Web; Engineering; MEDLINE","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaepi_broad","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002574423,0.0004519097,0.00105113,0.1018524,0.000638278,0.004581845,0.0005363748,0.0004698363,0.003143528],"category_scores_gemma":[0.01159561,0.0001651964,0.0009758287,0.1506267,0.0004865157,0.003762474,0.001054618,0.0003670571,0.001026073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001548866,"about_ca_system_score_gemma":0.001899607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005634394,"about_ca_topic_score_gemma":0.00623397,"domain_scores_codex":[0.9954397,0.0004516822,0.001023824,0.0004575407,0.00239055,0.0002367678],"domain_scores_gemma":[0.9876006,0.004388011,0.003569942,0.0004761802,0.003595277,0.0003700395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002880502,0.0001314144,0.5240113,0.009186739,0.00106052,0.000494971,0.001373507,0.001893395,0.002812791,0.009191608,0.0230752,0.4264805],"study_design_scores_gemma":[0.00002014614,0.00009395259,0.8825576,0.00153181,0.0007413025,0.001102323,0.002321121,0.004808538,0.002086709,0.003582138,0.1010511,0.0001033724],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.620804,0.2107956,0.004041679,0.004257984,0.0005194998,0.0001808141,0.06968897,0.001028174,0.08868331],"genre_scores_gemma":[0.8746153,0.08206897,0.003601301,0.0001957563,0.0006995927,0.0001184526,0.033806,0.00009133158,0.004803257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9989489,"threshold_uncertainty_score":0.01361507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290077507938273,"score_gpt":0.2363300080788458,"score_spread":0.2234292329994631,"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."}}