{"id":"W4319591818","doi":"10.1007/978-3-031-22532-1_94","title":"Fundamental Mass Transfer Correlations Based on Experimental and Literature Data","year":2023,"lang":"en","type":"book-chapter","venue":"The minerals, metals & materials series","topic":"Advanced ceramic materials synthesis","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Rio Tinto (Canada); Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mass transfer; Experimental data; Work (physics); Contrast (vision); Diffusion; Computer science; Gravimetric analysis; Transfer (computing); Mechanics; Thermodynamics; Statistical physics; Mathematics; Statistics; Chemistry; Physics; Artificial intelligence","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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001390423,0.001399631,0.001675996,0.0002973018,0.0006800286,0.001483176,0.001517933,0.0006573323,0.009605289],"category_scores_gemma":[0.00007487453,0.0009607006,0.0001895939,0.0001124268,0.0008606529,0.001084468,0.0006260406,0.0003578197,0.001187155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001380983,"about_ca_system_score_gemma":0.0001270227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005742532,"about_ca_topic_score_gemma":0.00004824726,"domain_scores_codex":[0.9944809,0.0004340726,0.00142706,0.001818082,0.0009990628,0.0008408303],"domain_scores_gemma":[0.9959923,0.0004788937,0.0004950473,0.002693882,0.0001213933,0.0002184829],"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":[0.000541344,0.00004836431,5.727075e-7,0.0002345246,0.00009778504,0.000108294,0.0002856579,0.00006706761,0.9755552,0.02098853,0.002023618,0.00004897497],"study_design_scores_gemma":[0.001383644,0.0005439587,0.00002393836,0.001465476,0.0007070896,0.0002092562,0.0003010319,0.0001551654,0.8725452,0.01816323,0.1019408,0.002561193],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.4429145,0.01242425,0.004336914,0.009714086,0.06928217,0.02051621,0.2564372,0.008080116,0.1762946],"genre_scores_gemma":[0.3627639,0.0007432179,0.005072634,0.001279754,0.002794466,0.0006614892,0.009121398,0.001064871,0.6164982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4402036,"threshold_uncertainty_score":0.9998754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0389312034517122,"score_gpt":0.2637722839520912,"score_spread":0.224841080500379,"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."}}