{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001317803,0.00008808103,0.0001621876,0.00003899982,0.00009908685,0.00004678183,0.0002337656,0.00004403856,0.000007125137],"category_scores_gemma":[0.00002617636,0.00003226325,0.00006308693,0.00009976995,0.0001012251,0.0002337966,0.0001398877,0.0001285141,5.47596e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006325414,"about_ca_system_score_gemma":0.000001769807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002683089,"about_ca_topic_score_gemma":0.0003070485,"domain_scores_codex":[0.9990745,0.0001860909,0.0002716901,0.00008304582,0.0002092199,0.0001754186],"domain_scores_gemma":[0.9986146,0.001088886,0.0002118122,0.00002145902,0.00003887478,0.00002437836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004973377,0.0002620728,0.7595943,0.00009466294,0.00004397196,0.00001706491,0.003804379,0.001199822,0.1884375,0.001734449,0.000003757863,0.04431067],"study_design_scores_gemma":[0.0002428579,0.0001451978,0.9854553,0.0002796658,0.000008434525,0.000008688845,0.01084402,0.000415454,0.001941171,0.0004822662,0.000105626,0.00007133332],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990673,0.0002430949,0.00002097582,0.0002723889,0.0001305627,0.000172194,0.00003087147,0.00000356145,0.00005905004],"genre_scores_gemma":[0.9956145,0.004113046,0.0001323888,0.00002064734,0.00008490291,0.00002289295,0.000009053379,8.967817e-7,0.000001618635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.225861,"threshold_uncertainty_score":0.1315657,"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."}}