{"id":"W1575259187","doi":"10.1111/tgis.12140","title":"Estimating Geographical PV Potential Using LiDAR Data for Buildings in Downtown San Francisco","year":2015,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Downtown; Roof; Footprint; Lidar; Photovoltaic system; Renewable energy; Environmental science; Meteorology; Remote sensing; Solar energy; Geography; 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":[],"consensus_categories":[],"category_scores_codex":[0.0006219821,0.0001063512,0.0001466059,0.0003064413,0.0001084427,0.0001295675,0.0007138071,0.00009299923,0.00003166976],"category_scores_gemma":[0.00006390458,0.0001164284,0.00004069651,0.0008218533,0.000039374,0.0007862776,0.00002996358,0.0002290172,0.000003127906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006146616,"about_ca_system_score_gemma":0.0001241279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001422657,"about_ca_topic_score_gemma":0.0005948981,"domain_scores_codex":[0.9988358,0.00004710698,0.0002877266,0.0003702211,0.0002084203,0.0002507212],"domain_scores_gemma":[0.9991543,0.00007553279,0.00005166429,0.0005690221,0.0000490561,0.0001004827],"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":[0.0001471398,0.001069548,0.01277736,0.0001316905,0.00009367619,0.0000755083,0.01070191,0.6914645,0.01053473,0.002824834,0.0006145066,0.2695646],"study_design_scores_gemma":[0.0007863865,0.00003097409,0.001502862,0.00002219093,0.000008939492,0.00002428269,0.00005201972,0.9945251,0.0002352644,0.001203903,0.001464032,0.0001439939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08755128,0.00005583867,0.911124,0.0002727627,0.0006265593,0.0002121945,0.00003381425,0.00006773362,0.0000558101],"genre_scores_gemma":[0.6142823,0.000001961326,0.3855539,0.00008189923,0.00004459039,0.00001195872,0.00001013473,0.000007073465,0.000006220223],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.526731,"threshold_uncertainty_score":0.4747811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06117897265697821,"score_gpt":0.3168379612912742,"score_spread":0.255658988634296,"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."}}