{"id":"W4210509668","doi":"10.3390/rs14030701","title":"Temperature Variation and Climate Resilience Action within a Changing Landscape","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; University of Toronto Mississauga; Natural Sciences and Engineering Research Council of Canada; Ministry of Water Resources; University of Toronto; Ministry of Environment; Mitacs; International Development Research Centre","keywords":"Livelihood; Climate change; Geography; Psychological resilience; Agriculture; Vegetation (pathology); Ecological resilience; Environmental resource management; Physical geography; Environmental science; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003433702,0.00006646289,0.00005975453,0.0000498049,0.0005148369,0.00003382635,0.00003040106,0.0000273293,0.00008005252],"category_scores_gemma":[0.00002236831,0.00006808157,0.00001268895,0.0002760507,0.00002056407,0.0001470662,0.0001216945,0.0001411948,0.00001440319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000118448,"about_ca_system_score_gemma":0.000005318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001077153,"about_ca_topic_score_gemma":0.00005892219,"domain_scores_codex":[0.9992446,0.00007793686,0.00009596178,0.0002096951,0.0001977447,0.0001741147],"domain_scores_gemma":[0.9997826,0.00002167629,0.00005611484,0.0001027526,0.000003615285,0.00003320605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005132411,0.00001080728,0.003816071,0.00001967921,0.00000604066,0.00002767012,0.01168718,0.02167794,0.8932396,0.00003914484,0.0003662569,0.06905825],"study_design_scores_gemma":[0.0003670689,0.00007497428,0.03947151,0.00003270127,0.00002182782,0.0003486558,0.001831398,0.9480647,0.008354593,0.0004336219,0.0007402785,0.0002587132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975489,0.00002313351,0.0007714931,0.0001892788,0.0001808911,0.000110664,0.00000209779,0.00005093799,0.001122585],"genre_scores_gemma":[0.9959088,0.00001148946,0.003644466,0.0001712752,0.00004815332,4.861027e-8,0.000009291756,0.000009174355,0.0001973072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9263867,"threshold_uncertainty_score":0.3959761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00831677028085043,"score_gpt":0.2130122711786913,"score_spread":0.2046955008978408,"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."}}