{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.0059601,0.0007208009,0.001106315,0.08551158,0.001737462,0.01025846,0.0008468409,0.001172062,0.01716696],"category_scores_gemma":[0.03196488,0.0003045581,0.001142028,0.09682449,0.001036275,0.006315412,0.006220334,0.0007528121,0.005071149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002974511,"about_ca_system_score_gemma":0.004207583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002195946,"about_ca_topic_score_gemma":0.002300875,"domain_scores_codex":[0.9889334,0.00193133,0.001044966,0.001227512,0.005418481,0.001444317],"domain_scores_gemma":[0.9489253,0.0239172,0.006989839,0.00238508,0.01388661,0.003895936],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009465604,0.0004612344,0.2908629,0.01002127,0.002511928,0.001167999,0.005092424,0.002216008,0.009647152,0.06189543,0.04418163,0.5709955],"study_design_scores_gemma":[0.0001511124,0.0003088451,0.6997274,0.003881935,0.001623778,0.001801228,0.01493196,0.00248138,0.004341396,0.06212352,0.208503,0.0001245441],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5460702,0.1416127,0.004384039,0.01618056,0.001685931,0.0003852872,0.01610193,0.0008500075,0.2727293],"genre_scores_gemma":[0.9465036,0.0332149,0.002671805,0.001064372,0.001372757,0.0001662338,0.007468731,0.00009085774,0.007446712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9940399,"threshold_uncertainty_score":0.05742913,"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."}}