{"id":"W2808262253","doi":"10.1016/j.fbp.2018.06.002","title":"Energy efficient improvements in hot air drying by controlling relative humidity based on Weibull and Bi-Di models","year":2018,"lang":"en","type":"article","venue":"Food and Bioproducts Processing","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":68,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China","keywords":"Weibull distribution; Relative humidity; Process engineering; Environmental science; Humidity; Energy (signal processing); Waste management; Materials science; Engineering; Thermodynamics; Statistics; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002679714,0.0004045388,0.0003208469,0.0003097559,0.0002033444,0.0004230705,0.0005109018,0.0004390403,0.000750942],"category_scores_gemma":[0.0003860899,0.0002123495,0.0005436502,0.000367789,0.0002057838,0.0007331189,0.0002864319,0.000333632,0.0001483937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003605555,"about_ca_system_score_gemma":0.000207782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00184286,"about_ca_topic_score_gemma":0.002042595,"domain_scores_codex":[0.9998987,0.00002001438,0.00000476787,0.00002129908,0.0000389269,0.00001631245],"domain_scores_gemma":[0.999882,0.00005908689,0.00001708001,0.00001392004,0.00002240496,0.000005567614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006253766,0.00004707297,0.0007898347,0.00007671874,0.00002165614,0.00003809218,0.00003426553,0.9560406,0.02071261,0.005994533,0.0002246728,0.01595743],"study_design_scores_gemma":[0.000001045985,0.000009721345,0.0001790009,0.000001475498,0.000004014873,0.000007839223,0.00000400025,0.9965288,0.00262857,0.0004467648,0.0001851344,0.000003609877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3168643,0.002200462,0.6702269,0.0001943481,0.000113443,0.00004392496,0.0001408,0.0002915034,0.009924396],"genre_scores_gemma":[0.9836159,0.0005990004,0.01216099,0.00002434427,0.00001031676,0.00002528021,0.00005057853,0.00004197341,0.003471603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00184286,"threshold_uncertainty_score":0.003664255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02177229729462653,"score_gpt":0.2165837998264093,"score_spread":0.1948115025317828,"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."}}