{"id":"W4283590172","doi":"10.11159/ffhmt22.133","title":"Experimental and Numerical Modelling of Condensed Atmospheric Air for Irrigation of Agricultural Crops","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer","topic":"Plant Surface Properties and Treatments","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Irrigation; Environmental science; Atmospheric model; Atmospheric sciences; Agricultural engineering; Meteorology; Agronomy; Engineering; Geography; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002891598,0.000521596,0.0005241652,0.0003624778,0.0005464507,0.001128148,0.0007736997,0.001533721,0.002849204],"category_scores_gemma":[0.0008181261,0.0002668766,0.0005932355,0.0004486987,0.0007517199,0.0007864992,0.0004692674,0.000745895,0.0003263443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000756235,"about_ca_system_score_gemma":0.0006137979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007194207,"about_ca_topic_score_gemma":0.003962652,"domain_scores_codex":[0.9998234,0.00002942024,0.00001182467,0.00004683727,0.00005294669,0.00003560916],"domain_scores_gemma":[0.9996302,0.0002101724,0.00004029495,0.0000307882,0.00006475811,0.00002383806],"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":[0.0001205927,0.0001534604,0.003778439,0.0002477043,0.00001796127,0.0001859228,0.0001112995,0.9613801,0.02481462,0.003290838,0.0006329304,0.005266109],"study_design_scores_gemma":[0.0000199112,0.00007393205,0.001062209,0.00001182345,0.000006896237,0.00002118534,0.00004095528,0.9927711,0.004271211,0.0005325322,0.00117466,0.00001365873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8951602,0.002180215,0.0592547,0.0009135718,0.0003815317,0.000266952,0.002812132,0.0004153879,0.03861529],"genre_scores_gemma":[0.9882973,0.0004909312,0.007783173,0.00003823627,0.00002581749,0.0001168947,0.0003897704,0.00003400033,0.002823963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007194207,"threshold_uncertainty_score":0.0143047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03461301052387257,"score_gpt":0.2146081084296404,"score_spread":0.1799950979057678,"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."}}