{"id":"W3133255480","doi":"10.18820/2415-0495/trp77i1.8","title":"Introducing the Green Book: A practical planning tool for adapting South African settlements to climate change","year":2021,"lang":"en","type":"article","venue":"Town and Regional Planning","topic":"Sustainability and Climate Change Governance","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Human settlement; Vulnerability (computing); Climate change; Adaptation (eye); Environmental planning; Environmental resource management; Geography; Settlement (finance); Hazard; Population growth; Process (computing); Population; Business; Computer science; Environmental science; Sociology; Archaeology; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001322976,0.000904857,0.0004407523,0.0009633835,0.001331578,0.003375728,0.001309192,0.00215028,0.120821],"category_scores_gemma":[0.004865062,0.0005903224,0.000402299,0.001230033,0.0006819473,0.004592159,0.002860798,0.002111584,0.02483861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007968404,"about_ca_system_score_gemma":0.001680283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001732043,"about_ca_topic_score_gemma":0.007397687,"domain_scores_codex":[0.9995586,0.00020013,0.00002647104,0.00004079699,0.0001308559,0.000043171],"domain_scores_gemma":[0.9977461,0.001626438,0.00007669369,0.0001078983,0.0002101988,0.0002326493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003894349,0.0001213294,0.0004180749,0.0003662501,0.000007286125,0.0007182484,0.004829819,0.003038458,0.001612232,0.01900492,0.7389995,0.2308448],"study_design_scores_gemma":[0.00001633995,0.00003170311,0.0003474218,0.0001558175,0.000002428774,0.000221613,0.00131327,0.001094406,0.0003147934,0.006299399,0.9901741,0.00002873349],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01332048,0.002275619,0.402056,0.02050298,0.004085301,0.0024199,0.006564011,0.02142674,0.5273489],"genre_scores_gemma":[0.04261228,0.003652604,0.6085088,0.0035924,0.0006407166,0.001539421,0.003415204,0.004782607,0.331256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.120821,"threshold_uncertainty_score":0.4041865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07493678356611552,"score_gpt":0.3104074906684753,"score_spread":0.2354707071023598,"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."}}