{"id":"W2793392767","doi":"10.1016/j.landusepol.2018.02.008","title":"Woodlots, wetlands or wheat fields? Agri-environmental land allocation preferences of stakeholder organisations in England and Ontario","year":2018,"lang":"en","type":"article","venue":"Land Use Policy","topic":"Forest Management and Policy","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; University of Sheffield","keywords":"Stakeholder; Arable land; Business; Land use; Environmental planning; Environmental resource management; Stakeholder analysis; Agriculture; Public relations; Economics; Geography; Political science; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001739765,0.00009374099,0.0002211356,0.0004577455,0.004135916,0.002745854,0.0005102589,0.0008574847,0.005409865],"category_scores_gemma":[0.004147863,0.0002147063,0.00016054,0.001039708,0.001899482,0.0009933085,0.001521461,0.0005520999,0.0002475695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01706296,"about_ca_system_score_gemma":0.01142164,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9150677,"about_ca_topic_score_gemma":0.9869633,"domain_scores_codex":[0.9979858,0.0005309705,0.00008885109,0.0001080027,0.0003374642,0.0009488212],"domain_scores_gemma":[0.996222,0.001282558,0.0005509128,0.00007254037,0.0006053644,0.001266633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001029849,0.0001253418,0.6331326,0.0003217882,0.00005744897,0.001783977,0.2965768,0.000840253,0.003585028,0.01957309,0.007795769,0.03517809],"study_design_scores_gemma":[0.00002859244,0.00007090517,0.5334532,0.0001538828,0.00001931457,0.000161826,0.4419849,0.0006548223,0.0002936299,0.001029538,0.02210126,0.00004807375],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989528,0.0001350878,0.00004423923,0.001415606,0.000003403622,0.00001144502,0.00008151947,8.970135e-7,0.008779774],"genre_scores_gemma":[0.9958619,0.0001284756,0.00005510475,0.0001958791,0.000001336868,0.000007182091,0.00002912861,0.000002170476,0.003718771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08493227,"threshold_uncertainty_score":0.1708649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03659269704701491,"score_gpt":0.237929693979819,"score_spread":0.201336996932804,"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."}}