{"id":"W4220704719","doi":"10.1002/cjce.24408","title":"Support for catalysis in <scp>C</scp>anada by the <scp>CIC C</scp>atalysis <scp>D</scp>ivision and <scp>C</scp>anadian <scp>C</scp>atalysis <scp>F</scp>oundation","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Alberta; Imperial Oil (Canada)","funders":"","keywords":"Catalysis; Pipeline (software); Political science; Engineering; Chemistry; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.009095321,0.001894629,0.002595554,0.002302433,0.002441493,0.002468554,0.005709049,0.0007835914,0.0001652445],"category_scores_gemma":[0.04362474,0.001670951,0.001057504,0.004263634,0.001100173,0.00187239,0.001050494,0.003094487,0.0001500551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002613788,"about_ca_system_score_gemma":0.002462595,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01484931,"about_ca_topic_score_gemma":0.006158411,"domain_scores_codex":[0.9857432,0.0009715955,0.003401468,0.002277279,0.003279841,0.004326588],"domain_scores_gemma":[0.9752398,0.01582852,0.002579354,0.002338251,0.00074843,0.003265659],"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":[0.000007339076,0.0002370437,0.002682142,0.000580858,0.0006654275,0.0004963232,0.01616965,0.1207817,0.7599299,0.000497402,0.09710794,0.0008442855],"study_design_scores_gemma":[0.004226087,0.0008444199,0.002779214,0.0006083297,0.002042595,0.002240926,0.01494043,0.08542632,0.4778956,0.001774811,0.4067177,0.0005035945],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886214,0.002418603,0.00221881,0.0006526408,0.002197998,0.001356303,0.00093495,0.0002398304,0.001359511],"genre_scores_gemma":[0.9891898,0.0001855098,0.002143266,0.001034716,0.00121655,0.0003463936,0.0006821249,0.0004498724,0.00475176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3096097,"threshold_uncertainty_score":0.9996706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006973720504398198,"score_gpt":0.2102550851827674,"score_spread":0.2032813646783692,"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."}}