{"id":"W4414882172","doi":"10.1111/jfpe.70221","title":"Advances in Soft Sensors for Smart Food Drying: Innovations, Challenges, and Industrial Perspectives","year":2025,"lang":"en","type":"article","venue":"Journal of Food Process Engineering","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Robustness (evolution); Soft sensor; Key (lock); Quality (philosophy); Product (mathematics); Process (computing); Process control; Food products","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":[0.00008990222,0.0001599361,0.0002886154,0.0003630843,0.00002396088,0.00001716791,0.0001458754,0.000148537,6.524821e-7],"category_scores_gemma":[0.0006356209,0.0001650682,0.00003962728,0.0003213437,0.00002654145,0.0003523362,0.00001883769,0.0004334809,7.587767e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008176815,"about_ca_system_score_gemma":0.00001310311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.06636e-8,"about_ca_topic_score_gemma":0.000002702246,"domain_scores_codex":[0.9991968,0.000002880366,0.0003531852,0.0001256351,0.0001037537,0.0002177785],"domain_scores_gemma":[0.9995385,0.0001476275,0.00007770574,0.0000782206,0.0001260608,0.00003185594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000149435,0.0001021932,0.0003194153,0.002561742,0.0003973593,0.00001005347,0.003589486,0.6526949,0.0410441,0.01382975,0.00007020943,0.2852313],"study_design_scores_gemma":[0.01229819,0.002388031,0.001003897,0.005221174,0.0002424676,0.0002042304,0.03350744,0.07076923,0.7404015,0.09269772,0.03892253,0.002343589],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8423224,0.1391523,0.01694549,0.0004550758,0.0003278216,0.0002512566,0.000005997821,0.0002845791,0.0002550361],"genre_scores_gemma":[0.9918036,0.005295711,0.002786145,0.000003248286,0.00007051742,0.00001455072,4.57961e-7,0.00002334192,0.000002471523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6993574,"threshold_uncertainty_score":0.6731284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02118153173785042,"score_gpt":0.2515861644977931,"score_spread":0.2304046327599427,"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."}}