{"id":"W4408016369","doi":"10.1139/cjfr-2024-0255","title":"Enhanced forest inventories in Canada: implementation, status, and research needs","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Ministère des Ressources naturelles et des Forêts (Québec); Canadian Forest Service","funders":"","keywords":"Forestry; Forest inventory; Forest management; Geography; Environmental resource management; Environmental science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.02155105,0.0005198939,0.0008337207,0.004424921,0.004799133,0.00767469,0.003765716,0.0007420007,0.002147299],"category_scores_gemma":[0.03605532,0.0004570072,0.0007169805,0.01587206,0.003375587,0.00360443,0.002828405,0.001999366,0.0002484051],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.1386997,"about_ca_system_score_gemma":0.3761142,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.99602,"about_ca_topic_score_gemma":0.9973979,"domain_scores_codex":[0.9866825,0.001600569,0.0008477507,0.001153783,0.007493978,0.00222144],"domain_scores_gemma":[0.9112281,0.01221668,0.003792106,0.001752081,0.06567752,0.005333608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002631122,0.0001799029,0.2193514,0.005730198,0.0002753954,0.0003283305,0.008882937,0.004998578,0.002079014,0.02192251,0.03309266,0.702896],"study_design_scores_gemma":[0.00005988352,0.0002220775,0.6253749,0.00828679,0.0003506668,0.0002770061,0.02626321,0.006050773,0.002828581,0.005021449,0.3249474,0.0003172724],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.4180194,0.2761643,0.01728668,0.142871,0.001023028,0.001378994,0.04154227,0.0009677865,0.1007466],"genre_scores_gemma":[0.7443695,0.1758617,0.04797912,0.007795685,0.0002019897,0.0004151295,0.01545332,0.0002186034,0.00770505],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.8613003,"threshold_uncertainty_score":0.9989863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03458393413148717,"score_gpt":0.348075207581044,"score_spread":0.3134912734495569,"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."}}