{"id":"W2605651763","doi":"10.3390/rs9040356","title":"Temperature Compensation for Radiometric Correction of Terrestrial LiDAR Intensity Data","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"PotashCorp (Canada); University of Saskatchewan","funders":"","keywords":"Compensation (psychology); Standard deviation; Focus (optics); Lidar; Remote sensing; Root mean square; Radiometric dating; Scanner; Mean squared error; Intensity (physics); Optics; Ranging; Range (aeronautics); Environmental science; Geodesy; Geology; Mathematics; Physics; Materials science; Statistics","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":[0.0005604124,0.0006151464,0.0005535914,0.0008162942,0.000252791,0.0004977965,0.001029893,0.0005573505,0.001624936],"category_scores_gemma":[0.001783683,0.0003340059,0.0005095675,0.001121586,0.0002422702,0.000737072,0.000509679,0.0006815903,0.001103971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004352709,"about_ca_system_score_gemma":0.0005005715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002196847,"about_ca_topic_score_gemma":0.003014734,"domain_scores_codex":[0.9987361,0.000123402,0.00005059947,0.0003100632,0.0006796379,0.000100297],"domain_scores_gemma":[0.9989702,0.0001411758,0.0001398778,0.0002155511,0.0005152539,0.00001787529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002357427,0.00008810599,0.005989913,0.0004531592,0.0001017905,0.0002119147,0.000258898,0.02986634,0.6599288,0.001487394,0.00301964,0.2983583],"study_design_scores_gemma":[0.00003087892,0.0001776578,0.03451311,0.00006025907,0.0001186214,0.0007277942,0.0001170888,0.2781216,0.6607507,0.000878873,0.0243274,0.0001760421],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1882061,0.001299876,0.7996847,0.0001495735,0.0005029122,0.0001178695,0.0005112501,0.005923425,0.003604251],"genre_scores_gemma":[0.6336094,0.0005548616,0.3597296,0.0001253138,0.00007589191,0.0001016572,0.001137734,0.0006730583,0.0039924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002196847,"threshold_uncertainty_score":0.005435944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04472811852519067,"score_gpt":0.2951789182208551,"score_spread":0.2504507996956645,"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."}}