{"id":"W4412871765","doi":"10.1101/2025.07.16.664478","title":"Tracking savanna vegetation structure in South Africa by extension of GEDI canopy metrics with Landsat, Sentinel-2, and PALSAR","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Canopy; Vegetation (pathology); Extension (predicate logic); Remote sensing; Geography; Tracking (education); Forestry; Computer science; Archaeology; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006604789,0.0004229423,0.0001603214,0.0005687258,0.0001669219,0.000362199,0.0002927699,0.0001614711,0.0003739717],"category_scores_gemma":[0.000687901,0.0001875088,0.0002665368,0.0005934341,0.0001321371,0.0004300027,0.0003290382,0.0001530163,0.00008434021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005092327,"about_ca_system_score_gemma":0.0003897409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02977755,"about_ca_topic_score_gemma":0.06187019,"domain_scores_codex":[0.9998745,0.00004564436,0.000006520552,0.00003794179,0.00001601797,0.00001941782],"domain_scores_gemma":[0.9997707,0.00006694319,0.00007417975,0.00002667121,0.00004657286,0.00001499293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001628832,0.0001319478,0.8471792,0.0001014345,0.0002007336,0.0002094817,0.0004661594,0.07667729,0.01458606,0.0002496513,0.0002817442,0.05975334],"study_design_scores_gemma":[0.00002253225,0.0002442369,0.6403641,0.00005728614,0.0001292915,0.0001309725,0.0006468748,0.3516716,0.005247304,0.000219614,0.001235618,0.00003061298],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980736,0.00004221182,0.001453411,0.00001488804,0.000001385586,0.000009642117,0.0001774926,0.00002707843,0.0002002535],"genre_scores_gemma":[0.9964582,0.00004307906,0.003195552,0.000003603407,0.000001016519,0.000009172621,0.0002116522,0.000002803003,0.00007483581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02977755,"threshold_uncertainty_score":0.05920845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009500619278179325,"score_gpt":0.2037509482929172,"score_spread":0.1942503290147378,"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."}}