{"id":"W2460423011","doi":"10.5194/isprs-archives-xli-b1-151-2016","title":"REDUCTION OF STRIPING NOISE IN OVERLAPPING LIDAR INTENSITY DATA BY RADIOMETRIC NORMALIZATION","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lidar; Normalization (sociology); Remote sensing; STRIPS; Histogram; Data reduction; Environmental science; Computer science; Mathematics; Statistics; Geography; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002029401,0.0009975344,0.0007629443,0.001771153,0.0003347895,0.0009320794,0.001248829,0.0004198093,0.0007850993],"category_scores_gemma":[0.004974125,0.0002734968,0.0008351297,0.002307586,0.0003675345,0.001090839,0.0009989481,0.0007698463,0.001165088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004228935,"about_ca_system_score_gemma":0.0006624275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003563863,"about_ca_topic_score_gemma":0.004519054,"domain_scores_codex":[0.9986518,0.0001913946,0.00008714416,0.000416129,0.0005400045,0.0001135131],"domain_scores_gemma":[0.9979854,0.0003229324,0.0002421306,0.0005138163,0.0009008194,0.00003490445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004235191,0.000244801,0.02634591,0.0004612026,0.0003294063,0.0002484278,0.000355507,0.05050299,0.09903569,0.001717428,0.01064289,0.8096923],"study_design_scores_gemma":[0.00006455402,0.0002035084,0.08733317,0.0001035057,0.0002788552,0.0006388386,0.0004060736,0.7278717,0.148579,0.003471976,0.03090824,0.000140697],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.201563,0.001548773,0.7840034,0.000256192,0.0003421609,0.0001816383,0.002079513,0.007382476,0.002642955],"genre_scores_gemma":[0.5365773,0.0006983965,0.4499768,0.0001996037,0.0001300596,0.0001968858,0.009313224,0.001065478,0.001842242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003563863,"threshold_uncertainty_score":0.01073265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01889176556535015,"score_gpt":0.2525285995474078,"score_spread":0.2336368339820576,"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."}}