{"id":"W2096828641","doi":"10.5589/m13-024","title":"Tree genera classification with geometric features from high-density airborne LiDAR","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Inha University; York University","keywords":"Lidar; Geography; Remote sensing; Cartography; Physical geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001765724,0.000167837,0.000228078,0.0002570843,0.0002932118,0.0001600857,0.0001868494,0.0001075948,0.0002891463],"category_scores_gemma":[0.000103317,0.0001388437,0.00007075817,0.0006836446,0.0002002893,0.0002161642,0.00001478759,0.0003410495,0.0002342701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003642469,"about_ca_system_score_gemma":0.0001820491,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2275114,"about_ca_topic_score_gemma":0.1189246,"domain_scores_codex":[0.998746,0.00006874827,0.0002946022,0.0002442136,0.000304814,0.0003415785],"domain_scores_gemma":[0.9984882,0.0000610935,0.0002946192,0.0003568058,0.0001355388,0.0006636918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000006233997,0.000004237581,0.0008147116,0.000001183133,0.00002567383,0.00005558226,0.0002068891,0.0001570115,0.008561862,0.000005689608,0.007967154,0.9821938],"study_design_scores_gemma":[0.0004102023,0.00008448216,0.9689193,0.00005469507,0.00007426798,0.0004575304,0.0003708399,0.005936739,0.0074165,0.000991041,0.01497511,0.0003092818],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9635458,0.00009966752,0.02781019,0.002333349,0.0002029527,0.0001352417,0.000003772127,0.00001517804,0.005853876],"genre_scores_gemma":[0.8494226,0.00001090017,0.1496969,0.0003206308,0.0002383681,1.250941e-8,0.000007629846,0.00002281215,0.0002801447],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9818845,"threshold_uncertainty_score":0.8971528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01092093391121276,"score_gpt":0.1943896454596081,"score_spread":0.1834687115483954,"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."}}