{"id":"W2734794023","doi":"10.3390/f8070253","title":"Lidar and Multispectral Imagery Classifications of Balsam Fir Tree Status for Accurate Predictions of Merchantable Volume","year":2017,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Ministère des Forêts, de la Faune et des Parcs; University of Lethbridge","keywords":"Balsam; Lidar; Multispectral image; Forestry; Canopy; Clipping (morphology); Diameter at breast height; Taiga; Dead tree; Remote sensing; Environmental science; Mathematics; Geography; Ecology; Botany; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001008069,0.00007303835,0.000124171,0.00002637956,0.0003362791,0.00002761381,0.0001512883,0.00004392854,0.00004779106],"category_scores_gemma":[0.000117061,0.00006893129,0.00004873711,0.00005227734,0.0004041855,0.0001789227,0.00006569489,0.00004932548,0.00001616345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002947815,"about_ca_system_score_gemma":0.00001821023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280122,"about_ca_topic_score_gemma":0.003769513,"domain_scores_codex":[0.9993323,0.00001180647,0.0001890901,0.0001822924,0.0001025792,0.0001819297],"domain_scores_gemma":[0.9991892,0.00005773403,0.0001964723,0.0004579799,0.00002159688,0.00007702997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009296055,0.0004573445,0.7262162,0.00009206803,0.00008311299,0.000001003677,0.001184817,0.001918785,0.1972479,0.0020485,0.02283536,0.04782194],"study_design_scores_gemma":[0.0002429722,0.00005263576,0.9596159,0.00001228002,0.00002529921,0.00000203574,0.00005564726,0.02934087,0.003390401,0.0009451301,0.006251717,0.00006507476],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9717573,0.00003361178,0.01954864,0.0004332147,0.0000797232,0.0004350593,0.0003157764,0.0000249255,0.007371755],"genre_scores_gemma":[0.9893371,0.00003933839,0.009038146,0.000004768374,0.00002415848,0.00001489977,0.00002377024,0.000009835941,0.001508006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2333997,"threshold_uncertainty_score":0.2810936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02369481989335874,"score_gpt":0.2836912518184138,"score_spread":0.259996431925055,"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."}}