{"id":"W4390272175","doi":"10.9734/ijecc/2023/v13i123720","title":"Assessing the Effectiveness of Climate-Resilient Rice Varieties in Building Adaptive Capacity for Small-Scale Farming Communities in Assam","year":2023,"lang":"en","type":"article","venue":"International Journal of Environment and Climate Change","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Norsk institutt for Bioøkonomi; National Rice Research Institute, Indian Council of Agricultural Research; Multiple Sclerosis Scientific Research Foundation","keywords":"Agriculture; Productivity; Climate change; Yield gap; Yield (engineering); Agricultural economics; Scale (ratio); Geography; Crop; Benefit–cost ratio; Climate resilience; Crop yield; Index (typography); Profitability index; Agricultural science; Business; Agroforestry; Environmental science; Production (economics); Agronomy; Economics; Economic growth; Ecology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009645793,0.0003341722,0.0002019907,0.000593121,0.001126606,0.0008773534,0.000655112,0.0004215669,0.001278976],"category_scores_gemma":[0.001715356,0.0001408378,0.0002249545,0.0005828614,0.0006945785,0.0006582123,0.001105038,0.0003688074,0.0001419294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001397093,"about_ca_system_score_gemma":0.001417156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006521551,"about_ca_topic_score_gemma":0.02783151,"domain_scores_codex":[0.9995108,0.0001982102,0.00002207807,0.00005074256,0.00009078023,0.0001274837],"domain_scores_gemma":[0.9990778,0.0002123742,0.0001823175,0.00004341358,0.0001275818,0.0003564695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008057595,0.003962031,0.8128809,0.0004544755,0.0001301569,0.00320307,0.03712058,0.00293006,0.02501893,0.00161408,0.0007326973,0.1111472],"study_design_scores_gemma":[0.0000209585,0.002973048,0.9500434,0.0000756052,0.00004105757,0.0002478786,0.04075692,0.001854655,0.001454771,0.0004055294,0.002098614,0.00002748636],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992766,0.00001826454,0.00004856365,0.00004166183,0.000001433491,0.00002643222,0.00001382532,0.00000227456,0.0005709928],"genre_scores_gemma":[0.9992869,0.00004200505,0.000431697,0.00001266384,0.000001722986,0.00002646195,0.00001734213,6.711139e-7,0.0001804923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006521551,"threshold_uncertainty_score":0.01296717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06747612695432548,"score_gpt":0.2843208747692363,"score_spread":0.2168447478149108,"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."}}