{"id":"W4399562243","doi":"10.1016/j.rse.2024.114248","title":"Corrigendum to “Characterizing satellite-derived freeze/thaw regimes through spatial and temporal clustering for the identification of growing season constraints on vegetation productivity” [Remote Sensing of Environment Volume 309 (2024) 114210]","year":2024,"lang":"en","type":"erratum","venue":"Remote Sensing of Environment","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada; University of British Columbia","funders":"","keywords":"Remote sensing; Vegetation (pathology); Satellite; Environmental science; Productivity; Identification (biology); Volume (thermodynamics); Cluster analysis; Satellite imagery; Computer science; Geology; Ecology; Artificial intelligence; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.0006209871,0.0004165847,0.0006664139,0.0000835064,0.0002694315,0.00005818954,0.0001454834,0.0001956028,0.00003601846],"category_scores_gemma":[0.0001150332,0.0003647903,0.0001904351,0.0001318713,0.0004315367,0.0001378953,0.00009316112,0.0003387067,0.00001052743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000667226,"about_ca_system_score_gemma":0.00005832578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004594865,"about_ca_topic_score_gemma":0.0008947188,"domain_scores_codex":[0.99719,0.0001208982,0.000958402,0.0007617921,0.0006091508,0.0003597473],"domain_scores_gemma":[0.9981932,0.000206158,0.0009242715,0.0005621313,0.0000407389,0.00007350987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001386566,0.00002357723,0.0005911743,0.001115081,0.0003456117,0.000009583279,0.002815963,0.00719003,0.01807598,0.000002850189,0.002006254,0.9676852],"study_design_scores_gemma":[0.0006817686,0.0009119863,0.3283623,0.004095937,0.001159473,0.00004221665,0.004160951,0.5223508,0.0132845,0.0003880791,0.1233266,0.001235351],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4077995,0.02373275,0.5005782,0.007148903,0.04928337,0.008079343,0.001547382,0.0001359187,0.001694669],"genre_scores_gemma":[0.9294669,0.00608806,0.05527309,0.0001889633,0.001272286,4.64722e-7,0.0008419581,0.00008757223,0.006780699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9664499,"threshold_uncertainty_score":0.9998804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02807535817687385,"score_gpt":0.2243750324430963,"score_spread":0.1962996742662224,"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."}}