{"id":"W2138434134","doi":"10.1139/cjfr-2013-0448","title":"National forest inventories in the service of small area estimation of stem volume","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Forest inventory; Statistics; Linear regression; Hectare; Forestry; Mathematics; Regression analysis; Geography; Environmental science; Forest management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002453057,0.0005638148,0.0004773911,0.003295698,0.0002804814,0.0006223482,0.0007963937,0.0001753136,0.004936399],"category_scores_gemma":[0.005187646,0.0002490081,0.0004323985,0.006457007,0.00015618,0.0008283305,0.0006026513,0.0003128615,0.00159701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001246042,"about_ca_system_score_gemma":0.002677403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2464654,"about_ca_topic_score_gemma":0.361117,"domain_scores_codex":[0.9988633,0.0002330409,0.000122049,0.0002265425,0.0004798601,0.0000752191],"domain_scores_gemma":[0.9959559,0.0007303323,0.0006610042,0.0008281193,0.001712124,0.0001124934],"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":[0.0003214325,0.000112017,0.4171804,0.0006591345,0.0005509437,0.0001778041,0.0005812469,0.02254255,0.003991122,0.01245973,0.1002264,0.4411972],"study_design_scores_gemma":[0.00006853272,0.00009386986,0.6737771,0.0002853801,0.0002047702,0.0003535022,0.0007333528,0.03513012,0.004264367,0.007336593,0.2776514,0.0001009792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1628625,0.00371193,0.1007798,0.0003553079,0.0002549636,0.0003755792,0.6893394,0.002299769,0.04002072],"genre_scores_gemma":[0.2938972,0.001692105,0.09210798,0.0001040597,0.00005789787,0.000524327,0.5998546,0.0004579331,0.01130389],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2464654,"threshold_uncertainty_score":0.4900616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05948476333397,"score_gpt":0.2907463822838488,"score_spread":0.2312616189498788,"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."}}