{"id":"W2893887237","doi":"10.2495/eid180151","title":"MEASURING FARMLAND LOSS: LESSONS FROM ONTARIO, CANADA","year":2018,"lang":"en","type":"article","venue":"WIT transactions on ecology and the environment","topic":"Agroforestry and silvopastoral systems","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph","keywords":"Urbanization; Sustainability; Land use; Government (linguistics); Population; Agriculture; Environmental planning; Population growth; Business; Urban planning; Land-use planning; Geography; Natural resource economics; Agricultural economics; Economic growth; Economics; Civil engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001855721,0.0004657175,0.0004848775,0.002300036,0.005122801,0.002709044,0.001875942,0.0004614444,0.002292734],"category_scores_gemma":[0.00518973,0.0002694252,0.0004475884,0.01000814,0.001426672,0.001070999,0.00122516,0.0006572016,0.00037534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08888758,"about_ca_system_score_gemma":0.07910624,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9986331,"about_ca_topic_score_gemma":0.9996258,"domain_scores_codex":[0.9976224,0.0001888823,0.0000983132,0.0001825107,0.001446636,0.0004611771],"domain_scores_gemma":[0.9921577,0.0005566091,0.0003740516,0.0002630738,0.006181029,0.0004674602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004087392,0.0002309051,0.7757366,0.0008151386,0.000235086,0.001499997,0.02122752,0.007117136,0.002098715,0.005093035,0.04307047,0.1424666],"study_design_scores_gemma":[0.00002266024,0.00007515983,0.9177356,0.000269396,0.00007774848,0.000127509,0.03121759,0.002766938,0.0006886962,0.0006974339,0.04626654,0.00005470508],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8644496,0.004554725,0.003691866,0.005256047,0.0001310584,0.0008666488,0.02026613,0.0001320245,0.1006519],"genre_scores_gemma":[0.954064,0.004778294,0.006995925,0.0008241925,0.00003342372,0.0001989918,0.008010795,0.00008176277,0.02501256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08888758,"threshold_uncertainty_score":0.6449276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01681875521511825,"score_gpt":0.1646525582602292,"score_spread":0.147833803045111,"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."}}