{"id":"W3163959050","doi":"10.1139/cjfr-2020-0529","title":"A design-based assessment of an expanded set of auxiliary information for forest growth estimation","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Estimation; Set (abstract data type); Forest inventory; Computer science; Environmental science; Satellite; National forest; Data set; Vegetation (pathology); Random forest; Satellite imagery; Data mining; Forest management; Forestry; Artificial intelligence; Geography; Agroforestry; Engineering; Systems engineering","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":[0.01085441,0.001078491,0.001200201,0.001255687,0.0002545259,0.0009751651,0.0009835904,0.0009548431,0.00141913],"category_scores_gemma":[0.02333517,0.0004813982,0.000994548,0.0006789425,0.000570917,0.001305487,0.001180036,0.0006458203,0.0002431413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004751715,"about_ca_system_score_gemma":0.001330757,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00129255,"about_ca_topic_score_gemma":0.001521225,"domain_scores_codex":[0.9959255,0.002757097,0.0001401702,0.0003359676,0.0007142394,0.0001270091],"domain_scores_gemma":[0.9780417,0.01532536,0.000941914,0.002855263,0.002594328,0.0002414787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001491844,0.0003795719,0.02020897,0.0003809679,0.0003389947,0.0001372946,0.0001025913,0.7385908,0.01578178,0.006203941,0.0004937788,0.2158896],"study_design_scores_gemma":[0.00008085511,0.000650021,0.006900206,0.00003707067,0.000131173,0.00006373387,0.00001542417,0.9822747,0.005221019,0.003630664,0.0009654862,0.00002966615],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1145509,0.0002694191,0.8830087,0.0001058045,0.00001695093,0.0001609787,0.000312176,0.0003194279,0.001255614],"genre_scores_gemma":[0.6066805,0.0001988692,0.3906244,0.00007815081,0.00004365415,0.000334047,0.001242907,0.00007174928,0.0007256521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9987075,"threshold_uncertainty_score":0.05740428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04697317104363392,"score_gpt":0.3343320407164151,"score_spread":0.2873588696727812,"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."}}