{"id":"W4323075787","doi":"10.54910/sabrao2023.55.1.7","title":"COTTON GENOTYPES APPRAISAL FOR MORPHO-PHYSIOLOGICAL AND YIELD CONTRIBUTING TRAITS UNDER OPTIMAL AND DEFICIT IRRIGATED CONDITIONS","year":2023,"lang":"en","type":"article","venue":"SABRAO Journal of Breeding and Genetics","topic":"Research in Cotton Cultivation","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics","keywords":"Biology; Cultivar; Irrigation; Agronomy; Drought tolerance; Crop; Chlorophyll; Crop yield; Fiber crop; Proline; Yield (engineering); Horticulture; Malvaceae","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005044826,0.00009142441,0.0001724869,0.00002738385,0.0003163879,0.0001017331,0.00007297608,0.00009545637,0.0000130755],"category_scores_gemma":[0.0004964898,0.00004041417,0.00003904253,0.0001646337,0.0001342832,0.00007440586,0.00005988686,0.0001375149,6.307253e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007451394,"about_ca_system_score_gemma":0.000009125742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008327216,"about_ca_topic_score_gemma":0.000008998893,"domain_scores_codex":[0.9991774,0.00003552161,0.0002341433,0.0001434141,0.0001589283,0.0002506039],"domain_scores_gemma":[0.9985132,0.001031445,0.0001246624,0.00001403377,0.0001871665,0.000129555],"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":[0.00005237626,0.00001917934,0.007626403,0.0000157482,0.00003973689,0.000003612922,0.00009586371,0.0002960952,0.9743403,0.0004895593,0.0007458886,0.01627531],"study_design_scores_gemma":[0.0006305909,0.001623851,0.9795492,0.00008722834,0.00004211474,0.0001245732,0.001650156,0.00535456,0.006972652,0.00287891,0.0009014166,0.0001847984],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972683,0.0005806988,0.0001192035,0.001746295,0.0000690565,0.0001146452,0.00006790612,0.00001692383,0.00001703507],"genre_scores_gemma":[0.9986287,0.0005423058,0.0004871655,0.00005688776,0.0002371601,0.000002930427,0.00002090458,0.000001373278,0.00002262165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9719228,"threshold_uncertainty_score":0.2433432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08916520232089156,"score_gpt":0.3232172477811154,"score_spread":0.2340520454602239,"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."}}