{"id":"W2791075437","doi":"10.5623/cig2017-401","title":"Building Extraction From Fused LiDAR and Hyperspectral Data using Random Forest Algorithm","year":2017,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Lidar; Hyperspectral imaging; Ranging; Remote sensing; Random forest; Extraction (chemistry); Computer science; Linear discriminant analysis; Algorithm; Fusion; Artificial intelligence; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001745354,0.0001296608,0.0001745351,0.00004796441,0.0002673268,0.0003844031,0.0002756482,0.00007377585,0.00001441061],"category_scores_gemma":[0.0002427653,0.0001352501,0.0000214126,0.00003067983,0.00006762102,0.0006799375,0.00008428984,0.0001255562,0.00002446398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005275313,"about_ca_system_score_gemma":0.00001012724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001531777,"about_ca_topic_score_gemma":0.00001958994,"domain_scores_codex":[0.9992307,0.00001880718,0.0001889621,0.0002366204,0.0001315492,0.000193378],"domain_scores_gemma":[0.9986374,0.0001020966,0.00008353785,0.001088156,0.00002436614,0.00006438702],"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":[0.00002646496,0.00003350286,0.0008519205,0.0000869268,0.0001601596,0.00004764257,0.0003835883,0.002937687,0.6832576,0.00009289558,0.0005081992,0.3116134],"study_design_scores_gemma":[0.0006984744,0.000004154477,0.02176382,0.0000670361,0.0000607027,0.00002508502,0.00004264517,0.9709442,0.004944127,0.0008921904,0.0004051449,0.0001523981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5322445,0.0001159806,0.4665885,0.00009266397,0.000295278,0.0001146079,0.00002526475,0.0001291959,0.0003940591],"genre_scores_gemma":[0.6283565,0.00002729567,0.3713299,0.000004259133,0.0002139266,7.746141e-7,0.00003024498,0.00002567859,0.00001139295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9680066,"threshold_uncertainty_score":0.5515341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05049766309727408,"score_gpt":0.2989953821201956,"score_spread":0.2484977190229215,"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."}}