{"id":"W2487309037","doi":"10.14288/1.0072500","title":"Scaling urban energy use and greenhouse gas emissions through LiDAR","year":2012,"lang":"en","type":"article","venue":"Open Collections","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; Lidar; Environmental science; Energy (signal processing); Scaling; Remote sensing; Geography; Physics; Geology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0001040729,0.0001034432,0.0001020576,0.00001622675,0.002685884,0.0006113378,0.0001574097,0.00005892489,0.0007730955],"category_scores_gemma":[0.0000552182,0.00009643182,0.00003178993,0.0006465265,0.0001157334,0.000680697,0.0002826501,0.00009381679,0.00005491221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007630387,"about_ca_system_score_gemma":0.00001496321,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03667431,"about_ca_topic_score_gemma":0.001621432,"domain_scores_codex":[0.9992127,0.00005339253,0.0001350646,0.0002237984,0.0001159658,0.0002590867],"domain_scores_gemma":[0.9993616,0.0001013495,0.00004401701,0.0002996795,0.000008308006,0.0001850502],"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":[0.00001445879,0.0004027265,0.0068303,0.000001854889,0.00003014398,0.000001355362,0.001799454,0.0001887174,0.0047714,0.001167638,0.9762415,0.008550458],"study_design_scores_gemma":[0.000198259,0.00002398864,0.004352815,0.00001116228,0.00003033444,0.00005138434,0.0002188754,0.0007051902,0.001518235,0.0008901613,0.9918063,0.0001932362],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08426328,0.00007361568,0.01044289,0.0006998847,0.0002550361,0.0004793317,0.00002633654,0.0001524664,0.9036071],"genre_scores_gemma":[0.6959105,0.0001385793,0.01852925,0.0003880255,0.0001243666,0.00002719088,0.000009720105,0.00003199107,0.2848403],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6187668,"threshold_uncertainty_score":0.9986125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02534824687732862,"score_gpt":0.2529487022400073,"score_spread":0.2276004553626786,"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."}}