{"id":"W2257491648","doi":"10.1017/s1355770x18000232","title":"Crop productivity and adaptation to climate change in Pakistan","year":2018,"lang":"en","type":"article","venue":"Environment and Development Economics","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council; International Development Research Centre","keywords":"Counterfactual thinking; Productivity; Adaptation (eye); Agriculture; Climate change; Propensity score matching; Matching (statistics); Business; Economics; Agricultural productivity; Natural resource economics; Environmental resource management; Agricultural economics; Agricultural science; Geography; Economic growth; Environmental science","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":[0.000127688,0.0000810639,0.00007784944,0.000007636704,0.000137935,0.00002906359,0.00004109872,0.00003055034,0.00004387952],"category_scores_gemma":[0.000001884681,0.00003327732,0.000005598094,0.0000309223,0.00004878279,0.000120358,0.00008927224,0.00002758949,0.00004223831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003489853,"about_ca_system_score_gemma":0.000001751615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004667689,"about_ca_topic_score_gemma":0.001294173,"domain_scores_codex":[0.9994258,0.0000107667,0.0001296787,0.0002455117,0.00003117662,0.0001570807],"domain_scores_gemma":[0.9998631,0.00001319856,0.00003452922,0.00001961463,0.000002716802,0.00006680693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000259247,0.0000300393,0.289334,0.000002729982,0.000002012042,5.305837e-7,0.002246872,0.000007677581,0.002435574,0.0001176866,0.000009905744,0.7057871],"study_design_scores_gemma":[0.00005697617,0.00007174212,0.970072,0.000005490501,7.811552e-7,0.000001217217,0.0003474147,0.00006329772,0.0007826428,0.00003650882,0.02844523,0.0001166879],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989345,0.00007567322,0.000003198143,0.000599062,0.00003439208,0.0002027122,0.000003139991,0.000008002888,0.0001392942],"genre_scores_gemma":[0.9980012,0.0006479531,0.001041266,0.0001063826,0.0001008601,0.00002431946,0.000008507484,4.577531e-7,0.00006907236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7056704,"threshold_uncertainty_score":0.135701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0244813721755336,"score_gpt":0.2148508373252821,"score_spread":0.1903694651497485,"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."}}