{"id":"W4388346905","doi":"10.1016/j.heliyon.2023.e22050","title":"Development of experimental error-Driven model for prediction of corrosion rates of amines based on their chemical structures","year":2023,"lang":"en","type":"article","venue":"Heliyon","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; SaskPower; Thammasat University","keywords":"Corrosion; Kriging; Amine gas treating; Experimental data; Gaussian; Work (physics); Materials science; Computer science; Biological system; Chemistry; Mathematics; Engineering; Metallurgy; Statistics; Computational chemistry; Machine learning; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003342488,0.000119472,0.0002358659,0.0001028037,0.00005445734,0.000009151422,0.0002326182,0.0000616238,0.00008293169],"category_scores_gemma":[0.0001246943,0.00009235901,0.00004449376,0.0001360088,0.0001206214,0.00006005892,0.00007722215,0.00003253843,0.000003729196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002576378,"about_ca_system_score_gemma":0.00006200004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000262468,"about_ca_topic_score_gemma":0.000001058587,"domain_scores_codex":[0.9988329,0.00004009319,0.0004128888,0.0002375973,0.0003135426,0.0001629077],"domain_scores_gemma":[0.999321,0.0001011441,0.0002622319,0.0001940203,0.00008956472,0.00003200475],"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":[0.000164404,0.00005150326,0.0003924982,0.0003069947,9.787917e-7,7.329478e-8,0.0009008143,0.05530677,0.9427078,0.00007869499,0.00003376727,0.00005564472],"study_design_scores_gemma":[0.0002222519,0.00008040517,0.001490665,0.000131908,0.000002181214,1.815914e-7,0.00008533274,0.3676666,0.6302271,0.00004259763,0.000005335537,0.00004537213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903936,0.00001817459,0.008741871,0.00001512365,0.0002667685,0.0002916401,0.0001899662,0.00005219664,0.00003070283],"genre_scores_gemma":[0.9628662,0.000002504774,0.03695199,0.00001168651,0.0000230867,0.00005567023,0.00005350946,0.00001270986,0.0000226149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3124807,"threshold_uncertainty_score":0.3766291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04679850051784602,"score_gpt":0.3146951678541799,"score_spread":0.2678966673363339,"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."}}