{"id":"W4229019268","doi":"10.1093/ej/ueac030","title":"Environmental Adaptation of Risk Preferences","year":2022,"lang":"en","type":"article","venue":"The Economic Journal","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; International Development Research Centre","keywords":"Decision maker; Subsistence agriculture; Adaptation (eye); Variation (astronomy); Test (biology); Panel data; Environmental resource management; Economics; Econometrics; Geography; Ecology; Psychology; Biology; Management 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008138178,0.0001397856,0.0002020869,0.0003116974,0.0001827813,0.000728388,0.0001542938,0.0003384813,0.003245618],"category_scores_gemma":[0.00417712,0.0001012608,0.0001784752,0.0004059174,0.0003284678,0.0003321531,0.000412735,0.000406465,0.000246092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002205044,"about_ca_system_score_gemma":0.0001179248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00134129,"about_ca_topic_score_gemma":0.001445319,"domain_scores_codex":[0.9994681,0.0002858813,0.00002275314,0.00009725145,0.00005965387,0.00006623453],"domain_scores_gemma":[0.9971245,0.001225879,0.0008385957,0.0005005474,0.0001535191,0.0001569039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004728166,0.0002245802,0.9453953,0.00004196086,0.000314247,0.0001833118,0.000852874,0.02052313,0.01055016,0.003464435,0.0004281616,0.01754895],"study_design_scores_gemma":[0.0000132303,0.0001717745,0.9826766,0.00001121817,0.0000333894,0.0001110404,0.0009755483,0.008263791,0.00159902,0.004829484,0.001282724,0.00003215492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977363,0.00001851525,0.000928782,0.0000341942,0.000001679295,0.000004590221,0.0002254041,0.000003054517,0.001047455],"genre_scores_gemma":[0.9996344,0.000007817981,0.0001652777,0.000009616922,8.751069e-7,0.000002514195,0.00009129113,4.174418e-7,0.00008786971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003245618,"threshold_uncertainty_score":0.0108577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243585746541586,"score_gpt":0.1727677105871594,"score_spread":0.1603318531217436,"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."}}