{"id":"W2787056864","doi":"10.5751/es-09844-230114","title":"The climate-smart village approach: framework of an integrative strategy for scaling up adaptation options in agriculture","year":2018,"lang":"en","type":"article","venue":"Ecology and Society","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":256,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Centre for International Agricultural Research; International Fund for Agricultural Development; Consortium of International Agricultural Research Centers","keywords":"Adaptation (eye); Agriculture; Climate change adaptation; Landscape ecology; Climate change; Ecology; Computer science; Regional science; Humanities; Geography; Physics; Biology; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0004409475,0.0001180703,0.0001649193,0.000003841621,0.0005701593,0.00004797071,0.0001300821,0.0003143382,0.00002652827],"category_scores_gemma":[0.00007945591,0.00003479094,0.00008707903,0.00022876,0.0002481196,0.0001249741,0.00003626642,0.0001798258,0.000001210623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002613655,"about_ca_system_score_gemma":0.000006795563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005046926,"about_ca_topic_score_gemma":0.004070826,"domain_scores_codex":[0.9991638,0.00009736112,0.0001878153,0.0002178223,0.00006452385,0.0002686831],"domain_scores_gemma":[0.9991625,0.0005006046,0.0001136418,0.00003931747,0.0001286738,0.00005529304],"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":[0.001260163,0.003080901,0.05810998,0.0003186301,0.000579242,0.000004058104,0.2252442,0.0007979002,0.3574299,0.1564126,0.02165552,0.1751069],"study_design_scores_gemma":[0.000329911,0.0009474494,0.815598,0.00003846393,0.00002712803,0.000007479204,0.1721575,0.00395094,0.0008234716,0.004629982,0.001263556,0.0002260825],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982964,0.0001267569,0.00004701152,0.0006168539,0.0001322123,0.0003888648,0.0001012758,0.00002270296,0.0002678727],"genre_scores_gemma":[0.9974432,0.0005037739,0.001368959,0.0001867493,0.0002290461,0.00005828564,0.0001551501,8.413544e-7,0.00005401005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7574881,"threshold_uncertainty_score":0.4385263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04464068655468155,"score_gpt":0.2879663506345634,"score_spread":0.2433256640798818,"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."}}