{"id":"W4254715848","doi":"10.3390/sci3020023","title":"Urban Sprawl and Growth Prediction for Lagos Using GlobeLand30 Data and Cellular Automata Model","year":2021,"lang":"en","type":"article","venue":"Sci","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Urban sprawl; Growth management; Cellular automaton; Land use; Metropolitan area; Geography; Land cover; Land use, land-use change and forestry; Urban planning; Urbanization; Environmental planning; Environmental resource management; Environmental science; Agriculture; Computer science; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001618797,0.00006232865,0.00007845487,0.000008287521,0.0001402278,0.00006149127,0.0001123055,0.00003854189,0.00003848913],"category_scores_gemma":[0.000008591034,0.0000512283,0.000008752213,0.00005847547,0.00001247791,0.0003831936,0.0003412119,0.00002424532,0.00000718452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001616635,"about_ca_system_score_gemma":0.000009381147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001497764,"about_ca_topic_score_gemma":0.000288587,"domain_scores_codex":[0.999386,0.00001259769,0.0000891001,0.0002870768,0.0000988683,0.0001263969],"domain_scores_gemma":[0.9996451,0.00001238672,0.00002755212,0.0002499207,0.000004948379,0.00006006378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005475697,0.000143187,0.9279315,0.0005675447,0.00007324444,0.00002693296,0.001122495,0.002473977,0.04893593,0.0002519216,0.01482071,0.003597732],"study_design_scores_gemma":[0.0003289747,0.00001381296,0.004911906,0.00002173566,0.00003547931,0.00001245457,0.00004642626,0.9902967,0.001680134,0.0005955638,0.00197772,0.00007911322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995849,0.0002425676,0.002874555,0.0001452503,0.00007370411,0.0001073982,0.0002217478,0.0000257863,0.0004600543],"genre_scores_gemma":[0.9972067,0.00005085256,0.002454096,0.00007029701,0.00005647931,0.000002832242,0.00009841815,0.000006689424,0.00005356468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9878227,"threshold_uncertainty_score":0.2089029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03013411393401876,"score_gpt":0.2370256499087221,"score_spread":0.2068915359747034,"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."}}