{"id":"W4390947779","doi":"10.3389/frsen.2023.1359181","title":"Corrigendum: An operational approach to near real time global high resolution mapping of the terrestrial human footprint","year":2024,"lang":"en","type":"erratum","venue":"Frontiers in Remote Sensing","topic":"Health, Environment, Cognitive Aging","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Footprint; Computer science; Ecological footprint; Remote sensing; High resolution; Environmental science; Resolution (logic); Environmental resource management; Geography; Artificial intelligence; Archaeology; Ecology; Sustainability; Biology","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":[0.003455508,0.002134461,0.0009110625,0.003743707,0.002824079,0.006353725,0.002869008,0.003258594,0.1358783],"category_scores_gemma":[0.0381386,0.0008952078,0.001099288,0.003341249,0.0015323,0.003013832,0.002603642,0.003622225,0.09797607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002273987,"about_ca_system_score_gemma":0.003662746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03401051,"about_ca_topic_score_gemma":0.04588401,"domain_scores_codex":[0.9971892,0.0004912387,0.000367028,0.0003891805,0.001465933,0.00009728971],"domain_scores_gemma":[0.9775081,0.003245138,0.0005111721,0.002343465,0.01580424,0.0005878201],"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":[0.00001327048,0.000008775647,0.0001772288,0.0001063801,0.000006657138,0.0001433091,0.00006448737,0.0002944662,0.0001981908,0.001781426,0.9714347,0.02577109],"study_design_scores_gemma":[0.00001157115,0.00002663188,0.0009747685,0.0001508743,0.00002047426,0.0003537509,0.0001868582,0.001640531,0.0007591447,0.00243132,0.9934043,0.00003972837],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.002057141,0.001842392,0.1520267,0.03370629,0.6321932,0.0008839702,0.02616382,0.0206483,0.1304783],"genre_scores_gemma":[0.02391986,0.005073172,0.2274575,0.01516164,0.04571132,0.001224147,0.03399578,0.0215415,0.6259152],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1358783,"threshold_uncertainty_score":0.4545582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02365325588215327,"score_gpt":0.2549943973094103,"score_spread":0.231341141427257,"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."}}