{"id":"W2133588660","doi":"10.5589/m08-057","title":"Remote sensing of boreal forest biophysical and inventory parameters: a review","year":2008,"lang":"en","type":"review","venue":"Canadian Journal of Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Taiga; Environmental science; Vegetation (pathology); Scale (ratio); Geography; Land cover; Forest inventory; Environmental resource management; Boreal; Satellite; Forest ecology; Identification (biology); Land use; Ecosystem; Forest management; Ecology; Cartography; Forestry; Agroforestry; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004564467,0.0004657499,0.001893578,0.0003247741,0.000223262,0.00004678372,0.0002627378,0.0002753578,0.000008245467],"category_scores_gemma":[0.0003234194,0.0004098458,0.0006639359,0.0006722804,0.000782818,0.0001106593,0.00005839986,0.0007978325,0.0000267919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005462978,"about_ca_system_score_gemma":0.0008669719,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02756673,"about_ca_topic_score_gemma":0.01143366,"domain_scores_codex":[0.9971172,0.0002989223,0.001260016,0.0004074173,0.0004007425,0.00051568],"domain_scores_gemma":[0.9968329,0.0001642441,0.001404868,0.0005735651,0.00008838069,0.0009360122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001260796,0.000002052264,0.000001611349,0.001698035,0.00005986118,0.0003103152,0.0000960705,0.000003707843,0.000003164107,0.000001110022,0.002808156,0.9950147],"study_design_scores_gemma":[0.0001142313,0.00006273239,0.00002307126,0.04784168,0.0006100012,0.01030719,0.00001954094,0.003218179,0.000004161437,0.0001747936,0.9372261,0.0003983217],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0007751702,0.9929937,0.003050976,0.0001971644,0.000239891,0.0004401256,0.00001076048,0.00001048256,0.002281743],"genre_scores_gemma":[0.0001497881,0.8909773,0.1084143,0.0001846972,0.0001370848,3.726686e-9,0.00001280825,0.00006612304,0.00005792723],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9946163,"threshold_uncertainty_score":0.9998353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03474644882700056,"score_gpt":0.2709395376492139,"score_spread":0.2361930888222133,"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."}}