{"id":"W3016329940","doi":"10.1139/cjfr-2020-0170","title":"National mapping and estimation of forest area by dominant tree species using Sentinel-2 data","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Norsk institutt for Bioøkonomi","keywords":"Deciduous; Forest inventory; Forestry; National forest; Biomass (ecology); Estimation; Environmental science; Forest management; Geography; Physical geography; Statistics; Mathematics; Ecology; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.000834711,0.00006145168,0.0001166379,0.0001332999,0.0002143739,0.00007084609,0.0003403442,0.00003550527,0.00009228581],"category_scores_gemma":[0.000527855,0.0000555965,0.0000227811,0.0003860993,0.0003948813,0.0002273997,0.00008576958,0.0002104246,0.000009014859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000155882,"about_ca_system_score_gemma":0.0003569938,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005557077,"about_ca_topic_score_gemma":0.0258471,"domain_scores_codex":[0.9987996,0.00005536797,0.0002569068,0.0001487644,0.0005117088,0.0002276347],"domain_scores_gemma":[0.9991261,0.0001003776,0.0001163172,0.0001545434,0.00009243159,0.0004102649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005149482,0.00005510883,0.7377943,0.00008433517,0.00007182088,0.00009672507,0.002655107,0.04500258,0.04966594,0.0009913098,0.1424292,0.02110203],"study_design_scores_gemma":[0.0005062571,0.00008214013,0.5238783,0.0001172471,0.00001547641,0.0001665518,0.0006832961,0.4365807,0.0009216599,0.001569657,0.03531987,0.0001588572],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884982,0.00009870871,0.004992149,0.002788567,0.00001963883,0.0000960723,0.00004711792,0.000001372191,0.003458178],"genre_scores_gemma":[0.9967093,0.00001456642,0.003048923,0.00003686531,0.00006461296,1.737557e-7,0.00002406766,0.000007397176,0.00009412622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3915782,"threshold_uncertainty_score":0.9919286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1362156830501993,"score_gpt":0.3264286287484662,"score_spread":0.190212945698267,"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."}}