{"id":"W2966436668","doi":"10.1073/pnas.1906419116","title":"Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions","year":2019,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":541,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Agriculture and Agri-Food Canada; AgreenSkills; Agence Nationale de la Recherche; Department for Environment, Food and Rural Affairs, UK Government; Biodiversa+; Government of the United Kingdom; Deutsche Forschungsgemeinschaft; Ministerio de Economía y Competitividad; Universidad de Alicante","keywords":"Agriculture; Diversity (politics); Crop; Geography; Crop diversity; Ecology; Biology; Environmental science; Political science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0003082414,0.0001969875,0.0003975992,0.0005879643,0.000280028,0.0005973949,0.0001996785,0.0002645383,0.0005153971],"category_scores_gemma":[0.0006442018,0.0002014919,0.0002900964,0.0003197579,0.000326246,0.0003497044,0.0008444183,0.0001852203,0.0000790204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003675967,"about_ca_system_score_gemma":0.0001725709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001998003,"about_ca_topic_score_gemma":0.004736041,"domain_scores_codex":[0.999742,0.00004952461,0.00001901186,0.0001087182,0.000027202,0.00005351957],"domain_scores_gemma":[0.9992546,0.0001786592,0.0002667086,0.00007784814,0.00005893482,0.0001631693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000703127,0.0001888074,0.6928407,0.0001129966,0.0003366945,0.000275238,0.0004050624,0.003148972,0.2898925,0.0001968593,0.00008970642,0.01180946],"study_design_scores_gemma":[0.000005167849,0.0001059874,0.9972789,0.000002769745,0.00002088644,0.00006601858,0.00008368459,0.001037421,0.001210181,0.00008042137,0.0001046297,0.000003971108],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996665,0.00006230216,0.0001120018,0.000003521487,3.675452e-7,0.000001730559,0.00001505053,0.000003564683,0.0001349858],"genre_scores_gemma":[0.9997305,0.00002882006,0.0001781266,0.000006474994,9.508674e-7,0.000002032318,0.00002527153,0.000001644856,0.00002610931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001998003,"threshold_uncertainty_score":0.003972769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02733335478552698,"score_gpt":0.2845200218569604,"score_spread":0.2571866670714335,"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."}}