{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006699008,0.0001037056,0.0002559417,0.00006635945,0.0004082009,0.00007746,0.0006419037,0.00003940436,0.000170511],"category_scores_gemma":[0.0001613176,0.00003779155,0.00008328205,0.0003192076,0.0002282848,0.0001973252,0.0004506263,0.0001202751,0.000002707632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007383919,"about_ca_system_score_gemma":0.00002117562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003312379,"about_ca_topic_score_gemma":0.0009830047,"domain_scores_codex":[0.9985538,0.0001013833,0.0004274665,0.0002103037,0.0004956413,0.0002114405],"domain_scores_gemma":[0.9986949,0.0002852522,0.0004012269,0.00002892424,0.00047506,0.0001146145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007614529,0.0005061019,0.6350334,0.000007100897,0.0003340962,0.0001657893,0.002151771,0.0009679581,0.3278889,0.003850173,0.007282385,0.02105081],"study_design_scores_gemma":[0.0001441435,0.002660013,0.9424191,0.00001613954,0.00001705402,0.000271218,0.002920477,0.00009995395,0.0006269969,0.0005281494,0.05015284,0.0001439489],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962932,0.0001792541,0.000005833779,0.002443352,0.0007371075,0.0001040924,0.00005074239,0.000006229938,0.0001802156],"genre_scores_gemma":[0.9989599,0.00005619677,0.0001591346,0.0003761369,0.0002716194,0.000007618415,0.00000183845,4.907461e-7,0.0001670644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3272619,"threshold_uncertainty_score":0.3139592,"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."}}