{"id":"W4398231446","doi":"10.1016/j.rse.2024.114210","title":"Characterizing satellite-derived freeze/thaw regimes through spatial and temporal clustering for the identification of growing season constraints on vegetation productivity","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Remote sensing; Vegetation (pathology); Satellite; Productivity; Environmental science; Growing season; Identification (biology); Cluster analysis; Satellite imagery; Computer science; Agronomy; Geography; Ecology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003285949,0.0003460437,0.0002776035,0.002284011,0.0006579885,0.0007582295,0.0004678272,0.0002285498,0.0004566461],"category_scores_gemma":[0.0009872072,0.0001394082,0.0004516532,0.00217774,0.0002761827,0.0002263776,0.0004416447,0.0002513411,0.0001463778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001909287,"about_ca_system_score_gemma":0.002677043,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7096009,"about_ca_topic_score_gemma":0.8134438,"domain_scores_codex":[0.9997777,0.00001293684,0.00001262262,0.00008022704,0.00004541755,0.00007101122],"domain_scores_gemma":[0.9995317,0.00007536668,0.00008395462,0.00004645563,0.0001914887,0.00007100838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002151618,0.0001241828,0.8967483,0.00008758622,0.0002470008,0.0001966351,0.0006315685,0.03225113,0.01229177,0.0007663632,0.003740895,0.05269939],"study_design_scores_gemma":[0.00001132785,0.00001966029,0.8782928,0.00002040903,0.00006745756,0.0000478667,0.0007442977,0.1167266,0.001312309,0.0002272435,0.002499514,0.00003044363],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903253,0.0001716515,0.002486508,0.00004282895,0.000006563724,0.00003011462,0.005619876,0.0001542362,0.001162847],"genre_scores_gemma":[0.9866654,0.00008786988,0.004011563,0.00001324187,0.000007264228,0.00001988551,0.008770266,0.00002621743,0.0003980848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7096009,"threshold_uncertainty_score":0.5842187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01689613545618973,"score_gpt":0.2376176454349835,"score_spread":0.2207215099787938,"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."}}