{"id":"W2791250395","doi":"10.1080/07373937.2018.1431658","title":"Enhancing drying efficiency and product quality using advanced pretreatments and analytical tools—An overview","year":2018,"lang":"en","type":"article","venue":"Drying Technology","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV","keywords":"Process engineering; Raw material; Electronic nose; Quality (philosophy); Environmental science; Food quality; Quality assurance; Computer science; Product (mathematics); Biochemical engineering; Pulp and paper industry; Engineering; Food science; Chemistry; Mathematics; Artificial intelligence; Operations management","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001085166,0.0002745984,0.00039184,0.0002372615,0.0001803155,0.0000401502,0.0002239044,0.000264284,0.000005937747],"category_scores_gemma":[0.0006450764,0.0002706596,0.00002509647,0.0005757174,0.0005603513,0.0003104103,0.0002297521,0.0003198308,0.000004093883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001446308,"about_ca_system_score_gemma":0.000005757249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000709831,"about_ca_topic_score_gemma":0.00001199204,"domain_scores_codex":[0.9984195,0.00001812331,0.0003491306,0.00056856,0.0001253774,0.0005193135],"domain_scores_gemma":[0.9991728,0.00007648204,0.00006687423,0.0005555729,0.0000575872,0.00007069424],"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":[0.00001031408,0.00003828904,0.002698904,0.0001352813,0.00003496005,0.000008382278,0.0001023024,0.0001484332,0.8512133,0.003316082,0.000001363592,0.1422924],"study_design_scores_gemma":[0.0004601992,0.0001849196,0.0005263418,0.00015379,0.0000403877,0.00006018801,0.0003162791,0.01737499,0.9739133,0.006167963,0.0003397232,0.0004619662],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900684,0.003345879,0.003745683,0.00009024391,0.00009206589,0.000236187,0.000004584565,0.002273167,0.0001437484],"genre_scores_gemma":[0.9744947,0.0003425338,0.02504061,0.00001780518,0.00003735415,0.00001557146,0.000002405804,0.00004136468,0.000007677831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1418304,"threshold_uncertainty_score":0.9999745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05974495711276941,"score_gpt":0.3456937390674372,"score_spread":0.2859487819546678,"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."}}