{"id":"W3004735248","doi":"10.22434/ifamr2019.0133","title":"Identifying risk in production agriculture: an application of best-worst scaling","year":2020,"lang":"en","type":"article","venue":"The International Food and Agribusiness Management Review","topic":"Agricultural risk and resilience","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Production (economics); Risk perception; Risk management; Identification (biology); Business; Latent class model; Agriculture; Survey data collection; Control (management); Agribusiness; Marketing; Economics; Perception; Microeconomics; Geography; Statistics; Finance; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02540098,0.002285033,0.002617105,0.009646544,0.002997135,0.005470548,0.002486999,0.001244743,0.004008351],"category_scores_gemma":[0.07188018,0.0008329381,0.0033468,0.0113558,0.005969243,0.004851677,0.005847446,0.002947153,0.0002750391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004652142,"about_ca_system_score_gemma":0.003147797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007047646,"about_ca_topic_score_gemma":0.006738473,"domain_scores_codex":[0.9722795,0.01784264,0.001931773,0.002480519,0.004962182,0.0005033514],"domain_scores_gemma":[0.9562886,0.03380294,0.004133564,0.002598945,0.002636175,0.0005396346],"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":[0.0006074597,0.001021423,0.110211,0.002567271,0.002224536,0.0008265526,0.02569142,0.07652646,0.002317559,0.1615694,0.006082478,0.6103543],"study_design_scores_gemma":[0.0001001671,0.001259597,0.0728709,0.001314377,0.00046367,0.0008055829,0.02511148,0.3259709,0.001673963,0.549829,0.02010987,0.0004904588],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1803799,0.00314698,0.7776658,0.002444153,0.0004476126,0.002509063,0.0009466722,0.0005733078,0.03188643],"genre_scores_gemma":[0.5437481,0.0009707672,0.452687,0.0001484651,0.0001017047,0.001134957,0.0002538642,0.0001029274,0.0008523383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02540098,"threshold_uncertainty_score":0.1343349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02639806714945376,"score_gpt":0.2508252259288311,"score_spread":0.2244271587793773,"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."}}