{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002903938,0.0002133586,0.0004230663,0.02226933,0.0009463942,0.001419976,0.0004476825,0.0002834631,0.002489366],"category_scores_gemma":[0.01284304,0.0001458198,0.0003788233,0.02955166,0.0006077115,0.001662448,0.001038095,0.0001935582,0.0002622601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001377592,"about_ca_system_score_gemma":0.002007746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01823726,"about_ca_topic_score_gemma":0.02111761,"domain_scores_codex":[0.9970917,0.0003444466,0.0005034507,0.0004364012,0.001367348,0.0002566206],"domain_scores_gemma":[0.9725811,0.01525172,0.005997082,0.001172702,0.004332398,0.0006651526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000106398,0.00001865507,0.943478,0.000327336,0.0001130743,0.0004376002,0.001907808,0.0004551758,0.002338461,0.002807769,0.001334711,0.04667502],"study_design_scores_gemma":[0.000003718875,0.00003015777,0.9901708,0.00002964354,0.0000606938,0.0002498517,0.0008866521,0.001112926,0.001049146,0.0003858075,0.006010436,0.00001007328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9832896,0.001726288,0.001477243,0.0003129969,0.00002516775,0.00004499011,0.004433369,0.00002972491,0.008660638],"genre_scores_gemma":[0.9954384,0.0008462443,0.000708325,0.00002773704,0.00003302061,0.00003737881,0.002253325,0.000007687753,0.0006479587],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9777307,"threshold_uncertainty_score":0.03626221,"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."}}