{"id":"W4392086284","doi":"10.1139/cjfr-2023-0118","title":"A new approach for spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Forest inventory; Geography; Forestry; Series (stratigraphy); Environmental science; Physical geography; Remote sensing; Forest management; Geology","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":[0.001289075,0.0009662925,0.0004512569,0.004726776,0.001033822,0.001547907,0.001354844,0.0003192124,0.002652223],"category_scores_gemma":[0.004416092,0.0004769264,0.000894213,0.009415458,0.0004722803,0.0007007195,0.001103795,0.001018285,0.0005427499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005423395,"about_ca_system_score_gemma":0.01165273,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9212065,"about_ca_topic_score_gemma":0.9606475,"domain_scores_codex":[0.9992205,0.00006873824,0.00004301562,0.0002565734,0.0003268886,0.00008431926],"domain_scores_gemma":[0.998353,0.0001434968,0.0001489831,0.000325257,0.0009630961,0.00006623803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001560586,0.000117354,0.1205305,0.000643085,0.000939952,0.0002721805,0.001336664,0.09336983,0.01336975,0.03794919,0.1167318,0.6145837],"study_design_scores_gemma":[0.00007991201,0.00007761465,0.3719619,0.0002970773,0.0003812612,0.0003860151,0.00174887,0.2918887,0.00731524,0.02085085,0.3046446,0.0003679881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07196803,0.001349359,0.7430536,0.001043143,0.0003966614,0.001130669,0.1514335,0.005713733,0.02391126],"genre_scores_gemma":[0.1779807,0.0009543817,0.7225461,0.0003277704,0.00007095789,0.0008150315,0.09065416,0.0006031792,0.006047603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07879347,"threshold_uncertainty_score":0.1585149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06259680156171814,"score_gpt":0.3047065365604617,"score_spread":0.2421097349987436,"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."}}