{"id":"W2027998681","doi":"10.1007/s11192-009-0424-0","title":"Citation structure of an emerging research area on the verge of application","year":2008,"lang":"en","type":"article","venue":"Scientometrics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":57,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thomson Reuters (Canada)","funders":"","keywords":"Pace; Commercialization; Demise; Citation; Bibliometrics; Nanotechnology; Data science; Engineering ethics; Computer science; Business; Engineering; Political science; Library science; Materials science; Marketing; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005074818,0.00008279118,0.0001400937,0.001601755,0.0003785123,0.00004810024,0.0009330355,0.0000492422,0.0003642982],"category_scores_gemma":[0.00386688,0.00005622538,0.00002551192,0.01001083,0.0005976851,0.0002229527,0.0001267893,0.0001466383,0.00003408414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000537939,"about_ca_system_score_gemma":0.00008777156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001209281,"about_ca_topic_score_gemma":0.000006550084,"domain_scores_codex":[0.996573,0.0003584166,0.0003093858,0.0003415595,0.002128602,0.0002890663],"domain_scores_gemma":[0.9978175,0.0005661933,0.0002661452,0.0006064281,0.0006740112,0.00006978986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001437424,0.00004473226,0.002252221,0.00001762551,6.617386e-7,3.96544e-7,0.001037607,0.005109129,0.9871975,0.003608203,0.0001286177,0.0005889197],"study_design_scores_gemma":[0.000121867,0.0002249442,0.05399594,0.00001656301,0.000002502573,0.000004190019,0.0002190052,0.01602959,0.9271874,0.001785341,0.0003216179,0.00009097543],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967789,0.00001886253,0.001793283,0.0001334936,0.0003419934,0.0002277123,0.00002861092,0.00002450438,0.0006526455],"genre_scores_gemma":[0.9954367,0.000006252606,0.004419215,0.00002461048,0.00003793994,0.000005715405,0.000005948312,0.000007532196,0.00005603437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06001005,"threshold_uncertainty_score":0.4809872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1288690603491131,"score_gpt":0.3997077030823721,"score_spread":0.270838642733259,"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."}}