{"id":"W2888248463","doi":"10.3390/rs10091338","title":"A Forest Attribute Mapping Framework: A Pilot Study in a Northern Boreal Forest, Northwest Territories, Canada","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of Lethbridge","funders":"","keywords":"Ecoregion; Lidar; Taiga; Remote sensing; Boreal; Forest inventory; Environmental science; Altimeter; Mean squared error; Vegetation (pathology); Geography; Physical geography; Forestry; Forest management; Statistics; Mathematics; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002956289,0.0002694624,0.0002818626,0.00007127005,0.0003957615,0.00007828412,0.0002238403,0.00006930897,0.000008295543],"category_scores_gemma":[0.0001239984,0.0002705629,0.00003647559,0.000787636,0.0002047516,0.00009705738,0.000188831,0.0003515968,0.0000504651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009029916,"about_ca_system_score_gemma":0.0001722771,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9804552,"about_ca_topic_score_gemma":0.9996842,"domain_scores_codex":[0.9977525,0.0001015682,0.000418741,0.0005892373,0.0004947262,0.0006432576],"domain_scores_gemma":[0.998845,0.0001202318,0.0001372333,0.0006683975,0.00004255623,0.0001866244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003611411,0.0001211475,0.9296327,0.000006084256,0.0000221999,0.000149538,0.001757385,0.0009538766,0.0003610918,0.000005898574,0.0002793635,0.06667456],"study_design_scores_gemma":[0.0003555548,0.00030739,0.9430476,0.0001022358,0.00001742135,0.00006690999,0.0008920339,0.04688011,0.00005223144,0.0005402321,0.007353004,0.0003852962],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987044,0.000008701168,0.008690909,0.0004214125,0.0002774555,0.0005506155,0.000009706599,0.00007892642,0.002918311],"genre_scores_gemma":[0.9894441,0.000002077887,0.009772683,0.0002131466,0.0004166776,8.677021e-8,0.00003284815,0.00004293455,0.00007538041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06628926,"threshold_uncertainty_score":0.9999747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964312869493459,"score_gpt":0.2402108772898836,"score_spread":0.220567748594949,"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."}}