{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.004478973,0.0003704438,0.0003329903,0.0004468519,0.0002446725,0.00989976,0.03617945,0.0001199992,0.000004180852],"category_scores_gemma":[0.001816227,0.0002890234,0.00001861594,0.001176617,0.0001210755,0.02693723,0.05546526,0.001078503,0.00002533536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004091833,"about_ca_system_score_gemma":0.001308471,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7710103,"about_ca_topic_score_gemma":0.9405145,"domain_scores_codex":[0.9948868,0.0004716903,0.0005113235,0.001925042,0.001543559,0.0006616107],"domain_scores_gemma":[0.9813123,0.0008969763,0.0002628411,0.01736196,0.00004631058,0.0001195477],"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":[0.00000472418,0.00002509155,0.0008401292,0.0005934124,0.00008163451,0.001514149,0.000005429002,0.000001181723,0.000002036446,0.0001590224,0.9960538,0.0007193653],"study_design_scores_gemma":[0.00009513152,0.00001081571,0.0007834646,0.0003603986,0.00007801117,0.00003667907,0.0001636825,0.05786401,0.000001106977,0.00009218852,0.9402178,0.0002966976],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000207434,0.002563129,0.001688864,0.002395824,0.001138773,0.0004307167,0.991507,0.00003110908,0.00003718493],"genre_scores_gemma":[0.0001168019,0.003587723,0.006809206,0.00007680233,0.0003550223,0.00001003763,0.9889668,0.00002389698,0.00005375475],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1695042,"threshold_uncertainty_score":0.9999562,"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."}}