{"id":"W4416452242","doi":"10.1016/j.foreco.2025.123372","title":"Species-specific modeling of tree diameter at breast height using tree height and relative density with implications for remote sensing-based forest inventory","year":2025,"lang":"en","type":"article","venue":"Forest Ecology and Management","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Department of Natural Resources","keywords":"Diameter at breast height; Tree (set theory); Temperate forest; Biomass (ecology); Forest inventory; Temperate rainforest; Temperate climate; Lidar","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001583171,0.0007976465,0.0004110211,0.0006358136,0.0003634625,0.000846669,0.001755799,0.0003800684,0.0007494615],"category_scores_gemma":[0.001911489,0.0004049107,0.0008088085,0.0008444613,0.0004344133,0.001008229,0.0005446297,0.0004966595,0.0002376801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002480393,"about_ca_system_score_gemma":0.002290844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2672329,"about_ca_topic_score_gemma":0.3359091,"domain_scores_codex":[0.9996517,0.00009525532,0.00001952455,0.0001468894,0.00004063739,0.00004602308],"domain_scores_gemma":[0.9993618,0.0002647036,0.0001265453,0.00007353513,0.0001276558,0.00004567415],"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":[0.00003951738,0.0001037638,0.2699542,0.00006345503,0.0002212366,0.0000699857,0.0001574057,0.7047774,0.001872183,0.002294678,0.0005640799,0.01988232],"study_design_scores_gemma":[0.000004574023,0.0000116672,0.04318605,0.00001166882,0.00002539185,0.00002428913,0.00006348849,0.9548558,0.000195755,0.001096536,0.0005072586,0.00001747309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8963059,0.0006169835,0.09848557,0.0003322121,0.00002869014,0.00005987241,0.001395066,0.000309105,0.002466562],"genre_scores_gemma":[0.9775638,0.0001767652,0.02029188,0.00004818107,0.00001104618,0.00003609381,0.0009358111,0.00003308025,0.0009032299],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2672329,"threshold_uncertainty_score":0.5313549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01691362751384732,"score_gpt":0.2245380006295124,"score_spread":0.2076243731156651,"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."}}