{"id":"W2789541004","doi":"10.1002/cjce.23165","title":"Chemical engineering research synergies across scientific categories","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Discipline; Curriculum; Chemical reaction engineering; Engineering; Engineering ethics; Chemistry; Library science; Political science; Social science; Sociology; Computer science; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.02653559,0.0001720716,0.0003305745,0.01298425,0.000566232,0.004817449,0.003955085,0.0001610367,0.0001751562],"category_scores_gemma":[0.05752427,0.0001102646,0.0001783103,0.05626706,0.001204985,0.000494227,0.0003602162,0.001170388,0.0001263936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000511651,"about_ca_system_score_gemma":0.00130604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007903156,"about_ca_topic_score_gemma":0.0001464414,"domain_scores_codex":[0.9918907,0.00006431151,0.0007756486,0.0003649844,0.005533287,0.001371075],"domain_scores_gemma":[0.9879988,0.00321339,0.0001567138,0.0007308535,0.006354313,0.001545962],"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":[0.0000409073,0.00004188402,0.002822024,0.00003349981,0.0001041971,0.0002134989,0.004730482,0.004428971,0.9258675,0.007344309,0.03785602,0.01651672],"study_design_scores_gemma":[0.0004751183,0.0001165499,0.001310067,0.00009188117,0.00001090021,0.0003730642,0.0004275809,0.03721379,0.8607656,0.003333913,0.09543391,0.0004475702],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954352,0.0009127375,0.0006131272,0.001252271,0.001478023,0.00007878712,0.00001351312,0.00001325228,0.0002030657],"genre_scores_gemma":[0.9981391,0.000002507831,0.000727563,0.0000210813,0.0008256915,0.000002733055,8.435084e-7,0.00002210285,0.0002583137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06510182,"threshold_uncertainty_score":0.9982027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3623810731150262,"score_gpt":0.4945005040236328,"score_spread":0.1321194309086066,"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."}}