{"id":"W3009453178","doi":"10.1016/j.jenvman.2020.110326","title":"Spatio-temporal evolution of agricultural land use change drivers: A case study from Chalous region, Iran","year":2020,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Land use; Agriculture; Land cover; Geography; Elevation (ballistics); Environmental resource management; Agricultural land; Land use, land-use change and forestry; Land management; Geographic information system; Population; Driving factors; Physical geography; Cartography; Environmental science; Engineering; Civil engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001099961,0.0001604428,0.0002531037,0.00003820856,0.00007889855,0.00003009657,0.0002126049,0.00003669095,0.0004119373],"category_scores_gemma":[0.000002154573,0.0001151277,0.0001044136,0.0001148521,0.00002305125,0.0007166039,0.0002544015,0.00009365435,0.0000714461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002477407,"about_ca_system_score_gemma":0.000001172028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002372464,"about_ca_topic_score_gemma":0.001033995,"domain_scores_codex":[0.9986361,0.00008004531,0.0004550769,0.000214477,0.0004547547,0.0001595892],"domain_scores_gemma":[0.9992004,0.00001525872,0.0004790731,0.0001508643,0.000002960374,0.0001514163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007860779,0.0004793989,0.9910506,0.00002214978,0.0001508221,0.002381039,0.004009817,0.000612954,0.0001458012,9.498318e-7,0.0003030369,0.000764804],"study_design_scores_gemma":[0.001425989,0.0006843116,0.9820389,0.00003572763,0.0001999933,0.000221326,0.01413633,0.000232015,0.00003469607,0.00001239948,0.0008232044,0.0001551289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989269,0.0001024982,0.00005992141,0.0002310641,0.0001138596,0.0004570821,0.0000205525,0.000007967426,0.00008013588],"genre_scores_gemma":[0.9994028,0.00008182022,0.0002151739,0.0001059601,0.0001504081,0.000006719373,0.000008788401,0.00001027383,0.00001808367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01012652,"threshold_uncertainty_score":0.469477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03072617676860138,"score_gpt":0.200196762397767,"score_spread":0.1694705856291656,"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."}}