{"id":"W4406994677","doi":"10.3390/thermo5010003","title":"Energy and Exergy Analyses Applied to a Crop Plant System","year":2025,"lang":"en","type":"article","venue":"Thermo","topic":"Sustainability and Ecological Systems Analysis","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Agricultural Adaptation Council","keywords":"Exergy; Crop; Environmental science; Energy crop; Agricultural engineering; Energy (signal processing); Agronomy; Agroforestry; Bioenergy; Biology; Process engineering; Engineering; Mathematics; Biotechnology; Biofuel; Statistics","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":[0.0001705292,0.00008526896,0.0001899683,0.00003759508,0.0001175096,0.00002691572,0.0001287609,0.00004986678,0.000512918],"category_scores_gemma":[0.00001306314,0.00006112205,0.00003775894,0.0003386114,0.00005623791,0.00002245678,0.0001755399,0.00002434513,0.00005541838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001726902,"about_ca_system_score_gemma":0.000004388179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003487311,"about_ca_topic_score_gemma":0.0005982441,"domain_scores_codex":[0.9992749,0.00004543279,0.00014792,0.0002544369,0.00009796027,0.0001793351],"domain_scores_gemma":[0.9996539,0.00004893531,0.00002673377,0.0001925335,0.000003478045,0.00007446112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001094111,0.001067154,0.1248115,0.0006936053,0.001250902,0.0002155121,0.004745407,0.04920198,0.2210036,0.2089433,0.02509198,0.3618809],"study_design_scores_gemma":[0.002108634,0.0005575059,0.6007589,0.0002066875,0.0007441387,0.00003159761,0.02243838,0.03305488,0.01983067,0.01367417,0.3045076,0.002086805],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9090367,0.00005089914,0.002825337,0.0001867968,0.00002923415,0.0001037037,0.000002096747,0.0000537263,0.08771149],"genre_scores_gemma":[0.9972695,0.00000329624,0.00005645049,0.0003042777,0.00001123517,0.00004768846,0.000001452061,0.000002961709,0.002303128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4759474,"threshold_uncertainty_score":0.5616092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01246581767068162,"score_gpt":0.2393293884877398,"score_spread":0.2268635708170582,"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."}}