{"id":"W4408293955","doi":"10.3390/f16030477","title":"Application of Machine Learning for Aboveground Biomass Modeling in Tropical and Temperate Forests from Airborne Hyperspectral Imagery","year":2025,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Université de Montréal; Université de Sherbrooke","keywords":"Hyperspectral imaging; Environmental science; Biomass (ecology); Temperate climate; Remote sensing; Tropical forest; Temperate forest; Temperate rainforest; Agroforestry; Tropics; Forestry; Geography; Ecology; Ecosystem; Biology","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":[0.0007120157,0.0007873286,0.0002683738,0.0005341618,0.0002223873,0.0004678669,0.0005180175,0.0004663999,0.0005152588],"category_scores_gemma":[0.001252508,0.0002544422,0.00054887,0.0005734015,0.0002463237,0.0005736842,0.0004065068,0.0005471535,0.0001639941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005992037,"about_ca_system_score_gemma":0.0005330546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01598695,"about_ca_topic_score_gemma":0.01549761,"domain_scores_codex":[0.9998547,0.00003394704,0.000009480142,0.00005568261,0.00002691644,0.00001916688],"domain_scores_gemma":[0.9997013,0.0001538309,0.00004293003,0.00002451916,0.00006513224,0.00001224619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003666316,0.0000481007,0.01063884,0.00002906901,0.0000578913,0.0000530977,0.00003147381,0.9509121,0.002543564,0.0003345502,0.0003003136,0.0350143],"study_design_scores_gemma":[0.000001148366,0.000004499929,0.001450445,0.000003004668,0.000002322835,0.000004341118,0.00000705958,0.997811,0.0003774607,0.0002621328,0.00007448439,0.00000213728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7623324,0.0009196188,0.2313105,0.0003850745,0.00007123549,0.00005259729,0.0007575335,0.0009070562,0.003263843],"genre_scores_gemma":[0.9686624,0.0001679131,0.02979805,0.00006026917,0.00001465614,0.00003975769,0.0005106714,0.00003080944,0.0007154333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01598695,"threshold_uncertainty_score":0.03178781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009980222697460229,"score_gpt":0.2455401657832297,"score_spread":0.2355599430857694,"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."}}