{"id":"W3034252022","doi":"10.3390/land9060193","title":"Tropical PeatLand Forest Biomass Estimation Using Polarimetric Parameters Extracted from RadarSAT-2 Images","year":2020,"lang":"en","type":"article","venue":"Land","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Space Agency","keywords":"Polarimetry; Remote sensing; Environmental science; Biomass (ecology); Scattering; Canopy; Leaf area index; Backscatter (email); Geography; Geology; Physics; Agronomy; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0001655393,0.000431729,0.0001899301,0.000964623,0.00008610749,0.0003673902,0.0001273013,0.0001361156,0.0002982882],"category_scores_gemma":[0.000237152,0.0001380111,0.0001954914,0.0004725257,0.0000831007,0.0003543115,0.000163364,0.0001042608,0.0002032548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001276366,"about_ca_system_score_gemma":0.0001684904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003951517,"about_ca_topic_score_gemma":0.007106203,"domain_scores_codex":[0.9999459,0.000009059566,0.000003973344,0.0000160893,0.00001558301,0.000009379821],"domain_scores_gemma":[0.9999384,0.00001458833,0.00001633314,0.000005664208,0.00001971046,0.00000543439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004019144,0.0002134669,0.3241765,0.0002943973,0.0001762637,0.0005404457,0.0002804127,0.1672684,0.1855508,0.0004178284,0.0005294404,0.3201501],"study_design_scores_gemma":[0.00001670153,0.00008857105,0.403821,0.0000428568,0.000124348,0.0003530263,0.0003962367,0.5545935,0.03904976,0.0004123399,0.001054582,0.00004707109],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9725823,0.0001816162,0.02562524,0.00001233075,0.000005593781,0.00001135596,0.0002881174,0.0001303554,0.001163126],"genre_scores_gemma":[0.9897519,0.0001507751,0.009332179,0.000003895183,0.000002898273,0.000007830412,0.0004294982,0.000009263953,0.0003115751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003951517,"threshold_uncertainty_score":0.007857025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02267297975432841,"score_gpt":0.2390119846948387,"score_spread":0.2163390049405103,"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."}}