{"id":"W4210717284","doi":"10.31407/ijees12.106","title":"USE OF SOYBEAN GENETIC RESOURCES TO CREATE HIGHLY ADAPTIVE VARIETIES","year":2022,"lang":"en","type":"article","venue":"International Journal of Ecosystems and Ecology Science (IJEES)","topic":"Agriculture and Biological Studies","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crop; Genetic resources; Agriculture; Geography; Productivity; Biology; Agricultural science; Biotechnology; Agronomy; Economic growth; Economics; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0006362988,0.0003456284,0.0003623378,0.001435586,0.0003823976,0.0006005372,0.0004791371,0.0002974257,0.001670652],"category_scores_gemma":[0.0003695273,0.000324606,0.0004285449,0.0009086706,0.0001863622,0.0002569256,0.0008441078,0.0009028728,0.0006545285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005114624,"about_ca_system_score_gemma":0.0004674082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001028001,"about_ca_topic_score_gemma":0.002003821,"domain_scores_codex":[0.9997085,0.00005220908,0.0000430189,0.00009185377,0.00006360292,0.00004083604],"domain_scores_gemma":[0.9998227,0.00002956862,0.0000399132,0.00002979121,0.00002317979,0.00005489056],"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":[0.0004695328,0.0001594592,0.002964376,0.000143321,0.00005481618,0.0005374169,0.0002507996,0.001028141,0.9625331,0.001055023,0.000214297,0.03058974],"study_design_scores_gemma":[0.001001046,0.004195258,0.2027627,0.0003502798,0.0009718504,0.004633031,0.001070308,0.0138807,0.6104572,0.003519475,0.1569021,0.0002561387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9591905,0.0009606858,0.02656233,0.0002078788,0.00006987548,0.0005747504,0.003185222,0.0007199576,0.008528783],"genre_scores_gemma":[0.8984833,0.001455786,0.07347587,0.0002398227,0.00002716212,0.0003374862,0.01931221,0.0004103521,0.006257895],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001670652,"threshold_uncertainty_score":0.005588889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02907192861296189,"score_gpt":0.2180792579545977,"score_spread":0.1890073293416358,"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."}}