{"id":"W4402777045","doi":"10.21608/jur.2024.307344.1164","title":"رصد وتحليل ظاهرة الزحف العمراني على الأراضي الزراعية باستخدام تقنيات (GeoAI) دراسة حالة مدن عواصم المحافظات الحبيسة في النصف الثاني من القرن العشرين Monitoring and Analyzing Urban Growth on Agricultural Lands Using (GeoAI) Techniques, Case Study: Capital Cities of Landlocked Governorates in the Second Half of the 20th Century","year":2024,"lang":"ar","type":"article","venue":"Journal of Urban Research /Journal of Urban Research","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Landlocked country; Agriculture; Capital (architecture); Geography; Capital city; Agricultural economics; Socioeconomics; Economics; Economic geography; Political science; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.02617831,0.000886524,0.001944543,0.003373166,0.001451882,0.001719828,0.002594064,0.0005653708,0.0001850574],"category_scores_gemma":[0.002387603,0.0004882316,0.0009012505,0.004568477,0.001672474,0.001484167,0.0004774728,0.009090909,0.00001057289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000511988,"about_ca_system_score_gemma":0.002363198,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01006939,"about_ca_topic_score_gemma":0.002510644,"domain_scores_codex":[0.9793887,0.006750049,0.003351926,0.0008652596,0.007385792,0.002258244],"domain_scores_gemma":[0.9860251,0.006474853,0.001711349,0.0009407695,0.00390643,0.0009414955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003116168,0.00148697,0.8752261,0.002173953,0.001835907,0.02442944,0.05081709,0.0005326783,0.0074916,0.0001266646,0.02742327,0.005340103],"study_design_scores_gemma":[0.008426151,0.02579086,0.5615865,0.02271082,0.001071648,0.03698376,0.3060951,0.00409814,0.01518156,0.0008544961,0.01501876,0.002182177],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9579003,0.03675434,0.0000029069,0.001615475,0.001267611,0.001106747,0.000219814,0.00001690012,0.001115903],"genre_scores_gemma":[0.9894628,0.005611453,0.0002255536,0.00002016828,0.003269608,0.000002075113,0.00001001833,0.00006999885,0.001328305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3136396,"threshold_uncertainty_score":0.9998481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06629572903630163,"score_gpt":0.3369887814424398,"score_spread":0.2706930524061382,"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."}}