{"id":"W4361996373","doi":"10.1007/978-3-031-24731-6_1","title":"Land-Use and Land Cover Change: Advancing with Geographic Information Science","year":2023,"lang":"en","type":"book-chapter","venue":"Advances in geographic information science","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Land use; Land cover; Geography; Land information system; Environmental planning; Perspective (graphical); Land-use planning; Cover (algebra); Land use, land-use change and forestry; Environmental resource management; Geographic information system; Land management; Cartography; Remote sensing; Environmental science; Computer science; Civil engineering; Engineering","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","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00184248,0.0003966066,0.0003459837,0.001966864,0.0007724523,0.0007505469,0.0008117634,0.0001536178,0.0001189132],"category_scores_gemma":[0.00009378405,0.0003029848,0.00005130338,0.002290816,0.001222381,0.06136947,0.0005593329,0.0003444238,0.0005522689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001419635,"about_ca_system_score_gemma":0.00009505066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007664598,"about_ca_topic_score_gemma":0.002791617,"domain_scores_codex":[0.9962114,0.00001133746,0.0006951307,0.0004754528,0.001825969,0.0007807018],"domain_scores_gemma":[0.9982988,0.0001027901,0.0006470877,0.0005156356,0.0001809201,0.0002548057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007608236,0.00001245742,0.9562625,0.0003328681,0.00001083824,0.000008326418,0.001891478,0.0085001,0.000005936864,0.005102,0.00001593572,0.02778148],"study_design_scores_gemma":[0.001832437,0.0004306934,0.6395953,0.001681836,0.00006406158,0.0001107507,0.0003436237,0.01964425,0.00003694852,0.00453094,0.3297183,0.00201091],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5752606,0.0009872022,0.0006013743,0.000383308,0.002218975,0.003886728,0.0003379986,0.0007006718,0.4156232],"genre_scores_gemma":[0.9802495,0.0163917,0.0009655454,0.001408199,0.00008409446,0.0001731978,0.0001577366,0.00003566368,0.0005343501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4150888,"threshold_uncertainty_score":0.9999422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009041915752607581,"score_gpt":0.2192279684161965,"score_spread":0.2101860526635889,"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."}}