{"id":"W2748841292","doi":"10.1016/j.apgeog.2015.11.013","title":"Corrigendum to “The spatial distribution of development in Europe and its underlying sustainability correlations” [Applied Geography Vol. 63, September 2015, 304–314]","year":2015,"lang":"en","type":"erratum","venue":"Applied Geography","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Geography; Distribution (mathematics); Sustainability; Economic geography; Regional science; Spatial distribution; Physical geography; Cartography; Ecology; Mathematics; Biology; Remote sensing","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":[0.003088795,0.002152135,0.002725456,0.004490141,0.003628289,0.005589817,0.003636743,0.006011802,0.07290953],"category_scores_gemma":[0.03629056,0.001111578,0.00260429,0.004309345,0.002382307,0.004036636,0.003115143,0.008457263,0.04612962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005937995,"about_ca_system_score_gemma":0.0050681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07871255,"about_ca_topic_score_gemma":0.1100789,"domain_scores_codex":[0.996258,0.0006177782,0.0006078316,0.0006076319,0.001634212,0.000274564],"domain_scores_gemma":[0.9791092,0.004738429,0.0007045619,0.001328032,0.0135671,0.0005528243],"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":[0.000005477998,0.000003022968,0.00003067507,0.00003939813,0.000004103696,0.00002717005,0.000009820606,0.00003126213,0.000009939297,0.0007825311,0.9975722,0.001484272],"study_design_scores_gemma":[0.00002051037,0.00001383224,0.001918718,0.0003459161,0.00002442461,0.00009097953,0.00009391034,0.0003228862,0.000138286,0.004659077,0.9923187,0.00005264453],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0001782792,0.004326462,0.001019844,0.1050285,0.8701869,0.00005293445,0.0034674,0.0004799434,0.01525971],"genre_scores_gemma":[0.009376958,0.01670012,0.003851619,0.152485,0.2674744,0.0003818665,0.007505027,0.001594952,0.54063],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.07871255,"threshold_uncertainty_score":0.2439067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02688003776455979,"score_gpt":0.2267927132344916,"score_spread":0.1999126754699318,"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."}}