{"id":"W2371595124","doi":"","title":"Investigation of China's nanotechnology study based on frequency analysis of key words","year":2003,"lang":"en","type":"article","venue":"Kexuexue yanjiu","topic":"Environmental Quality and Pollution","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Nanotechnology; Data science; Key (lock); Computer science; Political science; Materials science","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005507464,0.0001341305,0.0002783218,0.0002060839,0.00006587491,0.000004315628,0.0001856871,0.0001146785,0.001248655],"category_scores_gemma":[0.0001168459,0.0001283062,0.0001011102,0.001252602,0.0003353046,0.00009788094,0.00003595107,0.0001247966,0.00005604351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000123744,"about_ca_system_score_gemma":0.00001028688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008818976,"about_ca_topic_score_gemma":0.0004268515,"domain_scores_codex":[0.9985176,0.0002794989,0.000375645,0.0002891281,0.0003719249,0.0001661759],"domain_scores_gemma":[0.9991728,0.00007543415,0.0002259692,0.000467441,0.000003273724,0.0000550675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000018304,0.0003903438,0.9366895,0.000009060098,0.00009801814,0.000001829771,0.001103793,0.01448278,0.04469421,0.001904395,0.00003440391,0.000573359],"study_design_scores_gemma":[0.0004601034,0.0005475924,0.9465402,0.000009916645,0.0002692815,3.639766e-7,0.0003217259,0.001699663,0.04815076,0.001727302,0.0001144902,0.0001586429],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948453,0.00001296002,0.0009087771,0.0001481015,0.00005144686,0.0002361532,0.00002040657,0.00003205889,0.003744771],"genre_scores_gemma":[0.9985895,0.000002969992,0.001148708,0.0001289721,0.000003436843,0.00001190774,0.00001350547,0.00001087156,0.00009009679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01278312,"threshold_uncertainty_score":0.9996644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438309619232622,"score_gpt":0.2340653926714178,"score_spread":0.2196822964790915,"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."}}