{"id":"W3162408390","doi":"10.3390/rs13091830","title":"Retrieval of Arctic Vegetation Biophysical and Biochemical Properties from CHRIS/PROBA Multi-Angle Imagery Using Empirical and Physical Modelling","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Environment and Climate Change Canada","funders":"","keywords":"Remote sensing; Environmental science; Arctic; Vegetation (pathology); Hyperspectral imaging; Arctic vegetation; Leaf area index; Tundra; Geology; Ecology; Oceanography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003242083,0.0006140923,0.0002266368,0.0008835025,0.0003885454,0.0008655302,0.0005523196,0.0003300888,0.0004037915],"category_scores_gemma":[0.000553272,0.0003142983,0.0004973972,0.001016164,0.0002889707,0.0006438601,0.000298357,0.0003237928,0.0002342948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00217333,"about_ca_system_score_gemma":0.00227939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3659111,"about_ca_topic_score_gemma":0.433861,"domain_scores_codex":[0.9998417,0.00001429924,0.000006746308,0.00004379672,0.0000606261,0.00003276621],"domain_scores_gemma":[0.999781,0.00003354747,0.00004068902,0.00002664342,0.0001013646,0.00001697925],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003594363,0.0003451193,0.1877494,0.000313476,0.0001868216,0.0003277192,0.0003918521,0.6206899,0.05709671,0.001362606,0.002016152,0.1291608],"study_design_scores_gemma":[0.00001893846,0.0000284977,0.111727,0.00001688948,0.00002951032,0.00006166472,0.0001530058,0.8819615,0.004826872,0.0001844772,0.0009499328,0.00004178823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976448,0.0002283747,0.01834146,0.00008122993,0.000008683096,0.00004910412,0.001210453,0.0003844207,0.003248285],"genre_scores_gemma":[0.9765029,0.000180904,0.02140549,0.00002051909,0.000005184977,0.00002528881,0.001292892,0.00002803127,0.000538829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3659111,"threshold_uncertainty_score":0.7275625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03744579251472704,"score_gpt":0.2509424269615674,"score_spread":0.2134966344468403,"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."}}