{"id":"W2980604345","doi":"10.5194/isprs-archives-xlii-4-w18-395-2019","title":"MODELING THE IMPACT OF SURFACE CHARACTERISTICS ON THE NEAR SURFACE TEMPERATURE LAPSE RATE","year":2019,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Urban Heat Island Mitigation","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Digital elevation model; Surface (topology); Remote sensing; Digital surface; Environmental science; Feature (linguistics); Geology; Geometry; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002483106,0.0004574808,0.0002559859,0.0003366228,0.0001472862,0.0005719654,0.0004606814,0.000424087,0.0005904493],"category_scores_gemma":[0.0006999979,0.0002249425,0.0005509878,0.0004160974,0.000182103,0.0006383187,0.0002285849,0.0003652072,0.0001518958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003854907,"about_ca_system_score_gemma":0.0003734562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01298567,"about_ca_topic_score_gemma":0.00734953,"domain_scores_codex":[0.9999069,0.00001339181,0.000005536898,0.00003727321,0.00001920951,0.00001771311],"domain_scores_gemma":[0.9998211,0.00007399292,0.00003082732,0.00001896074,0.00004262092,0.00001245424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005912452,0.00005872217,0.02605636,0.00004169167,0.00004425828,0.0001144289,0.00003463881,0.9531469,0.007571374,0.0004988688,0.0002091966,0.01216444],"study_design_scores_gemma":[0.00000242152,0.0000108386,0.004526678,0.0000017782,0.000007545088,0.000009649219,0.000005743225,0.9943428,0.0008768526,0.00008421028,0.0001272657,0.000004309463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.934656,0.0003272786,0.06185104,0.00008046217,0.0000372471,0.00003259475,0.0004975596,0.0002893862,0.002228457],"genre_scores_gemma":[0.9962122,0.00008359239,0.002958533,0.000006768055,0.00000482478,0.00001686716,0.0002586043,0.00002037884,0.0004381202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01298567,"threshold_uncertainty_score":0.0258202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356161760197566,"score_gpt":0.2426681585349829,"score_spread":0.2291065409330073,"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."}}