{"id":"W4321437267","doi":"10.1007/s12393-023-09334-6","title":"Monitoring Visual Properties of Food in Real Time During Food Drying","year":2023,"lang":"en","type":"article","venue":"Food Engineering Reviews","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Tertiary Education Trust Fund","keywords":"Shelf life; Food systems; Artificial intelligence; Computer science; Fluidized bed; Process (computing); Computer vision; Food science; Engineering; Geography; Waste management; Chemistry; Food security","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002523893,0.0003242556,0.0003250696,0.0003017534,0.00009788103,0.0004868251,0.0003896102,0.0004686509,0.0007655603],"category_scores_gemma":[0.0005006522,0.0002309348,0.0002369019,0.0003304761,0.0002284646,0.0005612813,0.0002469271,0.000455434,0.0002332063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002512098,"about_ca_system_score_gemma":0.00009020398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008232351,"about_ca_topic_score_gemma":0.0009373678,"domain_scores_codex":[0.9998838,0.000009691115,0.000002828731,0.00003200205,0.00005703373,0.0000146937],"domain_scores_gemma":[0.9998013,0.0000857452,0.00004193875,0.00001755669,0.00004306493,0.00001041863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002482805,0.00004702839,0.002513127,0.0003647178,0.00002652572,0.0001181169,0.0001154203,0.006681056,0.9257815,0.0001976543,0.0005313155,0.0633752],"study_design_scores_gemma":[0.00003098469,0.0005813367,0.03967968,0.00005474291,0.00007779542,0.0007839896,0.0002733634,0.0820308,0.8675315,0.0009726964,0.007904087,0.00007907223],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.860173,0.01023034,0.1222056,0.0002434073,0.0002052811,0.00006388885,0.0004744625,0.0008179957,0.00558604],"genre_scores_gemma":[0.9675866,0.005588959,0.02369878,0.00009700977,0.00004703468,0.00002630828,0.0002477699,0.0001095566,0.00259797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008232351,"threshold_uncertainty_score":0.002561033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05726197308809398,"score_gpt":0.2347044817043807,"score_spread":0.1774425086162867,"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."}}