{"id":"W7083309710","doi":"10.1016/j.jag.2025.104877","title":"Individual tree species prediction using airborne laser scanning data and derived point-cloud metrics within a dual-stream deep learning approach","year":2025,"lang":"en","type":"article","venue":"International Journal of Applied Earth Observation and Geoinformation","topic":"Agricultural Innovations and Practices","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"Natural Resources Canada; National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Natural Resources and Forestry","keywords":"Deep learning; Tree (set theory); Basal area; Segmentation; Feature (linguistics); Workflow; Forest inventory; Pattern recognition (psychology); Metric (unit); Field (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005497613,0.001247025,0.0005062391,0.001167909,0.0003119227,0.0009081388,0.001471672,0.0009929356,0.001056631],"category_scores_gemma":[0.001430552,0.000382462,0.0008357633,0.0009180515,0.0004285987,0.001271454,0.001109506,0.001083057,0.0007609711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001358715,"about_ca_system_score_gemma":0.001385997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03645508,"about_ca_topic_score_gemma":0.06751111,"domain_scores_codex":[0.9997045,0.00003042373,0.00001302731,0.00011708,0.00008647405,0.0000484517],"domain_scores_gemma":[0.9994388,0.0001315963,0.00007364572,0.00007857681,0.0002264894,0.00005091059],"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":[0.0002545027,0.000334065,0.03709868,0.0001538983,0.0002095898,0.000164138,0.0001486249,0.6281469,0.01750393,0.001986793,0.00781672,0.3061823],"study_design_scores_gemma":[0.000005645014,0.00002047947,0.00220582,0.000007783829,0.00001036629,0.00001747954,0.00001665853,0.9941975,0.001618231,0.001358057,0.000534019,0.000007998749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3142947,0.0007219114,0.6678975,0.000584615,0.0001043307,0.0002027565,0.003576225,0.008432357,0.004185571],"genre_scores_gemma":[0.7957661,0.0002168015,0.1938249,0.0002320499,0.00004396305,0.0001591171,0.006365948,0.0001471601,0.003244004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03645508,"threshold_uncertainty_score":0.0724858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05589561472109763,"score_gpt":0.2555907403174948,"score_spread":0.1996951255963972,"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."}}