{"id":"W4412995160","doi":"10.1080/07373937.2025.2540729","title":"Role of AI and machine learning in advancing dryer design and scale-up","year":2025,"lang":"en","type":"article","venue":"Drying Technology","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Scale (ratio); Artificial intelligence; Process engineering; Machine learning; Computer science; Manufacturing engineering; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000246412,0.00007727945,0.0001601654,0.00008160532,0.00009849716,0.00001115732,0.00008899222,0.0001339,0.000003759229],"category_scores_gemma":[0.0001130859,0.00003749948,0.00001317659,0.000352181,0.00007830196,0.0000417921,0.0001278915,0.0002403013,4.625354e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000929991,"about_ca_system_score_gemma":0.000005785462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002219216,"about_ca_topic_score_gemma":0.0003156261,"domain_scores_codex":[0.9993939,0.00003469183,0.0001461907,0.0002046796,0.00003978971,0.0001806858],"domain_scores_gemma":[0.9997882,0.0001008991,0.0000364857,0.00003464693,0.00002205124,0.00001774126],"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":[0.00001768798,0.00001109309,0.07624778,0.000009486518,0.00000395854,7.355892e-7,0.00006467821,0.00017081,0.5434105,0.001055853,0.000004645045,0.3790027],"study_design_scores_gemma":[0.002073708,0.001369303,0.0720742,0.001137685,0.00007236833,0.00005726992,0.006173945,0.2462313,0.5295829,0.1298859,0.0103214,0.001020044],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949165,0.002272063,0.0007361082,0.001747801,0.00002418558,0.0000869878,9.404534e-7,0.0001142868,0.0001011313],"genre_scores_gemma":[0.9980116,0.0001372573,0.001687014,0.00007401576,0.000005832395,0.000007742819,0.000001674465,7.040039e-7,0.00007416395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3779827,"threshold_uncertainty_score":0.1529185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009789780097949365,"score_gpt":0.2201789948144586,"score_spread":0.2103892147165092,"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."}}