{"id":"W2890244366","doi":"10.3390/rs10091432","title":"Reply to Vauhkonen: Comment on Tompalski et al. Combining Multi-Date Airborne Laser Scanning and Digital Aerial Photogrammetric Data for Forest Growth and Yield Modelling. Remote Sens. 2018, 10, 347","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"West Fraser (Canada); Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"","keywords":"Remote sensing; Photogrammetry; Laser scanning; Yield (engineering); Geology; Environmental science; Laser; Optics; Materials science; Physics","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":[0.009330283,0.001510398,0.002094334,0.001499179,0.004739913,0.004207575,0.005168555,0.06079474,0.007074462],"category_scores_gemma":[0.0467186,0.001407984,0.001989045,0.001726134,0.006325483,0.006784917,0.003937198,0.07479525,0.01135657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004974003,"about_ca_system_score_gemma":0.006512094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01720436,"about_ca_topic_score_gemma":0.01844867,"domain_scores_codex":[0.9949646,0.001134947,0.0007973516,0.0009960164,0.00151153,0.0005955366],"domain_scores_gemma":[0.9798673,0.01156675,0.001383487,0.0006991021,0.004757674,0.001725713],"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":[0.00002913975,0.000008041742,0.0001305008,0.00004675283,0.00001166044,0.0001458257,0.00009293079,0.00001877226,0.00007068373,0.0005142471,0.9975985,0.001333138],"study_design_scores_gemma":[0.0000990277,0.00005198415,0.002067791,0.0004527013,0.00005177659,0.0006528803,0.0007680323,0.0002200512,0.0005640597,0.005070162,0.989856,0.0001454307],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001194859,0.001166775,0.00007133796,0.9706444,0.02741947,0.00001158664,0.0001394024,0.00004023014,0.0003872522],"genre_scores_gemma":[0.0007848742,0.0003701929,0.00007761981,0.9857537,0.01184508,0.00002884244,0.00002904465,0.00002071863,0.00109001],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.06079474,"threshold_uncertainty_score":0.04934388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05216566930090319,"score_gpt":0.2831425740537333,"score_spread":0.2309769047528301,"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."}}