{"id":"W4416932085","doi":"10.33480/techno.v22i2.7200","title":"LAND COVER CHANGE PREDICTION USING CELLULAR AUTOMATA AND MARKOV CHAIN MODELS","year":2025,"lang":"","type":"article","venue":"Jurnal Techno Nusa Mandiri","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Land cover; Cellular automaton; Markov chain; Land use; Population; Land use, land-use change and forestry; Identification (biology); Software; Cover (algebra)","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"],"consensus_categories":[],"category_scores_codex":[0.0005887693,0.0003889665,0.000427626,0.0002517715,0.0005878267,0.0002664175,0.0004127391,0.0004037526,0.000414062],"category_scores_gemma":[0.000006106551,0.0003398863,0.000100291,0.000584385,0.00006633157,0.001179788,0.0006595415,0.0003337682,0.0000849297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002950107,"about_ca_system_score_gemma":0.00002559655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009662754,"about_ca_topic_score_gemma":0.000171682,"domain_scores_codex":[0.9976403,0.00005518693,0.0005410662,0.0006931394,0.0004809146,0.0005893629],"domain_scores_gemma":[0.9990124,0.00003221697,0.0002355321,0.0005313252,0.00001640163,0.0001721142],"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":[0.0008913258,0.001423476,0.8359256,0.005302845,0.0009137418,0.0005664024,0.003411775,0.02131882,0.02819185,0.00122668,0.002320439,0.09850702],"study_design_scores_gemma":[0.001236309,0.0001080116,0.02310661,0.0009631918,0.0002368707,0.00005109079,0.0001136557,0.9640957,0.001548367,0.001768389,0.00637648,0.0003953889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876593,0.003066008,0.002640124,0.0006733963,0.0007168838,0.000776372,0.0001969362,0.0001659133,0.00410509],"genre_scores_gemma":[0.9965668,0.002305246,0.0002246775,0.0002453814,0.000228905,0.00003127785,0.0000124145,0.00002809453,0.0003572196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9427768,"threshold_uncertainty_score":0.9999053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02118764415775146,"score_gpt":0.2287218310525028,"score_spread":0.2075341868947514,"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."}}