{"id":"W7096481551","doi":"","title":"THE DETERMINANTS OF FARMLAND","year":2008,"lang":"en","type":"article","venue":"","topic":"Agricultural Economics and Policy","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Productivity; Agricultural productivity; Crop; Production (economics)","routes":{"ca_aff":false,"ca_fund":false,"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":[0.0006406173,0.0001931877,0.0002465206,0.0009291361,0.001068883,0.001044036,0.0006183435,0.0002630098,0.01344482],"category_scores_gemma":[0.003072348,0.0001521802,0.0002198569,0.002611783,0.0003674517,0.0005592728,0.0009898914,0.000598379,0.001064724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01051519,"about_ca_system_score_gemma":0.02202151,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9686132,"about_ca_topic_score_gemma":0.9807257,"domain_scores_codex":[0.9995134,0.00006628593,0.00001450449,0.00006021891,0.0001929538,0.0001527199],"domain_scores_gemma":[0.9965334,0.0003005097,0.0005690492,0.0001005068,0.001580618,0.0009160065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000129009,0.0001053049,0.6119476,0.0001934958,0.0001624619,0.0002266629,0.0005450869,0.002455488,0.0001606675,0.01641314,0.3067937,0.06086731],"study_design_scores_gemma":[0.00002241548,0.00003235666,0.9004024,0.0001355911,0.0000421615,0.00009430719,0.001182036,0.002755174,0.0001595259,0.001926723,0.09322624,0.00002108993],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4091015,0.004347751,0.00240233,0.02439146,0.0002162723,0.0002450233,0.4972273,0.0002452103,0.06182322],"genre_scores_gemma":[0.8514164,0.003183167,0.002516009,0.0007550021,0.00006733947,0.0001471284,0.09942509,0.00005518051,0.04243474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9686132,"threshold_uncertainty_score":0.07629347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02619000918577269,"score_gpt":0.2048161503805008,"score_spread":0.1786261411947281,"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."}}