{"id":"W1877183157","doi":"10.1139/cjfr-2013-0123","title":"Improving forest field inventories by using remote sensing data in novel sampling designs","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sampling (signal processing); Sampling design; Forest inventory; Field (mathematics); Computer science; Data set; Set (abstract data type); Remote sensing; Data mining; Systematic sampling; Statistics; Environmental science; Forest management; Mathematics; Artificial intelligence; Geography; Agroforestry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02199795,0.0009564674,0.001119459,0.001173661,0.0004137494,0.0007931927,0.001258478,0.00112544,0.001111907],"category_scores_gemma":[0.03226847,0.000898989,0.001052607,0.0009213906,0.0009811565,0.001536934,0.001677155,0.0007809831,0.0003496017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006686201,"about_ca_system_score_gemma":0.0009233966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000695705,"about_ca_topic_score_gemma":0.001437122,"domain_scores_codex":[0.9859447,0.01046066,0.0007780204,0.001454318,0.001059507,0.0003027652],"domain_scores_gemma":[0.9630726,0.02555778,0.003834322,0.004461229,0.002593967,0.000480073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003328244,0.00177034,0.04901161,0.0007547015,0.0006503845,0.0001941864,0.0005771903,0.3704408,0.05953987,0.0318609,0.001028229,0.4808436],"study_design_scores_gemma":[0.001717173,0.005580693,0.02526355,0.0001379605,0.000432669,0.0003514962,0.0001278569,0.8869737,0.03356599,0.03909564,0.006528822,0.0002245202],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09468403,0.0001659104,0.9036928,0.00007438125,0.00004029217,0.0004879747,0.0001457937,0.0002049287,0.0005039696],"genre_scores_gemma":[0.2122282,0.0001328627,0.7857816,0.00009067589,0.00007076624,0.001018092,0.0002907368,0.0000389324,0.0003480988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02199795,"threshold_uncertainty_score":0.1163377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.15910584770238,"score_gpt":0.3503903200561373,"score_spread":0.1912844723537574,"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."}}