{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002059086,0.00007252196,0.0001287579,0.0008228952,0.0004245988,0.0001103282,0.0002952852,0.00004139135,0.0002024368],"category_scores_gemma":[0.0004375309,0.00006912224,0.00001835155,0.001951948,0.0004824672,0.0001430215,0.00006918902,0.0006264704,0.000009911521],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003168107,"about_ca_system_score_gemma":0.008414991,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9935566,"about_ca_topic_score_gemma":0.9999041,"domain_scores_codex":[0.9978815,0.0002425931,0.0003243095,0.0001449745,0.000629452,0.0007771888],"domain_scores_gemma":[0.9986148,0.0003273045,0.00004588477,0.0001946982,0.0002066135,0.0006107321],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009433362,0.000005075621,0.9630871,0.000007921292,0.000006928745,0.0000224851,0.00100409,0.0002614256,0.0001543566,0.00270609,0.02250452,0.01023053],"study_design_scores_gemma":[0.0002853927,0.00005075909,0.9203851,0.00004402324,0.000001956987,0.000005274629,0.006521212,0.00004883197,0.0004560685,0.007381238,0.06476175,0.00005843133],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850799,0.0002339881,0.00004264178,0.003624015,0.00008020748,0.0002090939,0.000006780879,0.000001144037,0.01072217],"genre_scores_gemma":[0.9989381,0.00007938649,0.0001202729,0.00007395894,0.00003312639,0.00000418055,0.000003783349,0.000007445974,0.0007397053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04270208,"threshold_uncertainty_score":0.9972064,"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."}}