{"id":"W6894278899","doi":"10.5683/sp3/culupz","title":"SMAPVEX19-21 Massachusetts Vegetation Optical Depth, Version 1","year":2024,"lang":"en","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"","keywords":"Vegetation (pathology); Radiometer; Canopy; Water content; Hydrology (agriculture); Tree canopy; Radiometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006024307,0.0007128251,0.0006184351,0.0007037388,0.0001291311,0.0003008409,0.0008104465,0.0009618354,0.0001027645],"category_scores_gemma":[0.0004491056,0.0006927968,0.0003344896,0.0005656251,0.0001808229,0.000233234,0.0003480706,0.001155659,0.07706725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006697762,"about_ca_system_score_gemma":0.0003049542,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006706892,"about_ca_topic_score_gemma":0.04308803,"domain_scores_codex":[0.9960609,0.0002076321,0.0006000143,0.001060685,0.001368873,0.0007019027],"domain_scores_gemma":[0.9974672,0.0001967777,0.0002285113,0.001561061,0.0001793464,0.0003671161],"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":[0.0000888928,0.0001107929,0.000006411721,0.0005780131,0.0001904988,0.0005842115,0.00002887517,0.000009914407,0.00008903064,0.0000883702,0.9977162,0.0005087483],"study_design_scores_gemma":[0.0004393136,0.0001008006,0.001177129,0.0005073863,0.001028822,0.00005624431,0.00002154502,0.00007878648,0.00008066136,0.0003296807,0.9954685,0.0007111423],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000008954999,0.0007225782,0.000005145728,0.000303081,0.0008588337,0.0004789494,0.9868741,0.0004663322,0.010282],"genre_scores_gemma":[0.00006970492,0.0001577267,0.0003314003,0.0001957228,0.0009100648,0.00009596039,0.9978626,0.0002448844,0.0001319345],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07696448,"threshold_uncertainty_score":0.9999076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01937817179918453,"score_gpt":0.278751172882055,"score_spread":0.2593730010828705,"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."}}