{"id":"W4388672214","doi":"10.3390/rs15225352","title":"Evaluation of Tree-Growth Rate in the Laurentides Wildlife Reserve Using GEDI and Airborne-LiDAR Data","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Jet Propulsion Laboratory; National Aeronautics and Space Administration","keywords":"Lidar; Environmental science; Wildlife; Canopy; Tree canopy; Remote sensing; Forestry; Forest inventory; Forest ecology; Forest dynamics; Forest management; Geography; Ecosystem; Physical geography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006596806,0.000282918,0.0002108463,0.001421689,0.0004834931,0.0006107105,0.0004943122,0.0001635965,0.0004525252],"category_scores_gemma":[0.001216166,0.0001407485,0.0002278481,0.001100329,0.0002253706,0.0004010152,0.0003374167,0.0001330724,0.0001322891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002547968,"about_ca_system_score_gemma":0.001981207,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7362888,"about_ca_topic_score_gemma":0.8759988,"domain_scores_codex":[0.9996878,0.00003665306,0.00001761199,0.0000885669,0.0001100091,0.0000593618],"domain_scores_gemma":[0.9993418,0.0001326475,0.00009533975,0.00004350646,0.0002752611,0.0001113791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007360058,0.00005834013,0.9633443,0.00004137507,0.00004641054,0.000163077,0.0002284124,0.01061353,0.005185545,0.0001047209,0.0002081595,0.01993241],"study_design_scores_gemma":[0.000006334122,0.00003350191,0.9556651,0.00001297398,0.00001952491,0.00009695403,0.0004194922,0.04152858,0.001570288,0.00002094252,0.0006093576,0.00001695835],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980887,0.00004559282,0.0004902707,0.000009633889,0.000001110836,0.0000105858,0.0006827436,0.00003551839,0.0006358955],"genre_scores_gemma":[0.9967179,0.00002938253,0.001861968,0.000004638765,9.614938e-7,0.000007110965,0.001171218,0.000005549683,0.0002012903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2637112,"threshold_uncertainty_score":0.5305284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09849392008271803,"score_gpt":0.3268393866287392,"score_spread":0.2283454665460212,"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."}}