{"id":"W6947997048","doi":"10.48577/jpl.3k0snr","title":"MONITORING FOREST BIOMASS DYNAMICS IN THE LAURENTIDES RESERVE, CANADA, USING LIDAR DATA AND RADAR IMAGERY","year":2024,"lang":"en","type":"dataset","venue":"JPL Data","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Lidar; Biomass (ecology); Radar; Forest inventory; Temporal resolution; Ancillary data; Radar imaging","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000488042,0.0008288165,0.0004059536,0.003767654,0.001549114,0.001111321,0.001389089,0.0004125054,0.002994125],"category_scores_gemma":[0.001531103,0.0003426653,0.0003755154,0.006780102,0.0004898662,0.0004267671,0.0008375531,0.000626475,0.001599543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01710076,"about_ca_system_score_gemma":0.02056346,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9921979,"about_ca_topic_score_gemma":0.9967526,"domain_scores_codex":[0.9995128,0.0000201793,0.00002447431,0.0001113102,0.0002210236,0.000110221],"domain_scores_gemma":[0.998767,0.00008171007,0.00007567678,0.00008934379,0.0007978134,0.0001883998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004052153,0.0004286692,0.2177517,0.001104498,0.0003279141,0.0007548409,0.001294206,0.02200588,0.004742841,0.003155125,0.6536837,0.09434551],"study_design_scores_gemma":[0.0002990708,0.00005291579,0.5466993,0.0004366827,0.0001217158,0.00020958,0.002738894,0.03167747,0.004873874,0.001269487,0.4114265,0.000194423],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.07874065,0.0003928004,0.0009912205,0.0004201649,0.00002459987,0.0001486148,0.9123905,0.0009181806,0.005973229],"genre_scores_gemma":[0.05245927,0.0003116239,0.004179514,0.00007775672,0.00000696018,0.0001160809,0.9396317,0.00007573098,0.003141304],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01710076,"threshold_uncertainty_score":0.1240752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.179878275829817,"score_gpt":0.392402371579222,"score_spread":0.212524095749405,"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."}}