{"id":"W2766075242","doi":"10.5558/tfc2017-030","title":"Updating Canada’s National Forest Inventory with multiple imputations of missing contemporary data","year":2017,"lang":"en","type":"article","venue":"The Forestry Chronicle","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Forest Service","funders":"","keywords":"Missing data; Statistics; Forest inventory; Inference; Random forest; Econometrics; Geography; Environmental science; Cartography; Mathematics; Computer science; Forestry; Forest management","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":[0.02242752,0.0007668615,0.0009979958,0.002817892,0.002734379,0.002520853,0.002705173,0.0005735499,0.00177323],"category_scores_gemma":[0.05412874,0.000640123,0.001102348,0.008572439,0.0007109609,0.001558329,0.001431415,0.001712349,0.0004990387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009890412,"about_ca_system_score_gemma":0.02397775,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9279092,"about_ca_topic_score_gemma":0.9658788,"domain_scores_codex":[0.9925805,0.003317015,0.0004830839,0.001230632,0.001769818,0.0006189279],"domain_scores_gemma":[0.9670869,0.007834302,0.003279179,0.006476705,0.01469176,0.0006311621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002416895,0.0001397054,0.7115349,0.00028634,0.001115315,0.0002343746,0.001903758,0.04883071,0.001539899,0.003555234,0.03043596,0.2001821],"study_design_scores_gemma":[0.0001105512,0.0001628159,0.7124192,0.0004296449,0.0004767014,0.0001609852,0.0021947,0.2266047,0.00282263,0.007711753,0.04663417,0.0002721901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.50148,0.002134807,0.4359732,0.003613689,0.0006976495,0.0009843659,0.04284361,0.002124722,0.010148],"genre_scores_gemma":[0.6512939,0.000629075,0.3179766,0.000631901,0.00009865814,0.000445657,0.02582256,0.0002979237,0.002803696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0720908,"threshold_uncertainty_score":0.1450307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04189030067848621,"score_gpt":0.2735071009998322,"score_spread":0.231616800321346,"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."}}