{"id":"W4413054501","doi":"10.1038/s41597-025-05750-x","title":"Correction: Global Impacts Dataset of Invasive Alien Species (GIDIAS)","year":2025,"lang":"en","type":"erratum","venue":"Scientific Data","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Armed Forces","funders":"","keywords":"Alien; Ecology; Alien species; Invasive species; Geography; Data science; Biology; Computer science; Political science","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":[0.005164322,0.002374469,0.001824745,0.008310511,0.002186398,0.005409539,0.003231876,0.003296919,0.1757354],"category_scores_gemma":[0.0727659,0.001454266,0.001614502,0.007650842,0.001297581,0.003181568,0.003443277,0.006270357,0.09435707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004209521,"about_ca_system_score_gemma":0.009659989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09051948,"about_ca_topic_score_gemma":0.08430195,"domain_scores_codex":[0.9947724,0.0006394804,0.0009553224,0.0007081474,0.002483796,0.0004409371],"domain_scores_gemma":[0.9495419,0.009277969,0.002495179,0.005380477,0.03161722,0.00168733],"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":[0.00001564569,0.000002570595,0.00009277948,0.00005031217,0.000006323214,0.00001784796,0.000005720894,0.00004538261,0.000009657651,0.0001548062,0.9983373,0.001261561],"study_design_scores_gemma":[0.00007440964,0.000005978996,0.001607063,0.0002701767,0.00002626677,0.00005705076,0.0000660346,0.0002419572,0.0001620067,0.001051817,0.9964094,0.00002788882],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"dataset","genre_scores_codex":[0.0007467867,0.0006680173,0.001989524,0.03527163,0.5408369,0.0001345285,0.4005075,0.003288234,0.01655682],"genre_scores_gemma":[0.02074089,0.002869304,0.01617702,0.04650014,0.04699539,0.001327174,0.4958421,0.0124293,0.3571187],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1757354,"threshold_uncertainty_score":0.5878934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05863488910597562,"score_gpt":0.3023484650543872,"score_spread":0.2437135759484116,"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."}}