{"id":"W4410573777","doi":"10.1038/s41597-025-05184-5","title":"Global Impacts Dataset of Invasive Alien Species (GIDIAS)","year":2025,"lang":"en","type":"article","venue":"Scientific Data","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Armed Forces","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Alien; Biodiversity; Invasive species; Alien species; Taxon; Ecology; Ecosystem; Ecosystem services; Introduced species; Geography; Livelihood; Global change; Environmental resource management; Invertebrate; Biology; Climate change; Environmental science; Agriculture; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005855473,0.0001009797,0.0001260127,0.00003408963,0.0001775381,0.000160418,0.001732103,0.00003652032,0.04509652],"category_scores_gemma":[0.0003464282,0.00008887736,0.0000258836,0.001016618,0.0008178073,0.0004725957,0.002812489,0.00003841312,0.001648847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002514464,"about_ca_system_score_gemma":0.0000554631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003178122,"about_ca_topic_score_gemma":0.004511253,"domain_scores_codex":[0.9985075,0.00003093933,0.0002332048,0.0005631062,0.0003890398,0.0002762354],"domain_scores_gemma":[0.9978735,0.00003478227,0.00009061413,0.001892235,0.00001571886,0.00009312031],"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":[0.000006321383,0.00006800846,0.02074577,0.0000127914,0.00000771037,0.000002274346,0.00002713427,0.000002566388,0.003783033,0.001147719,0.9736938,0.0005028727],"study_design_scores_gemma":[0.0001911813,0.000009511034,0.1619212,0.00001639356,0.00001868538,0.000001741333,0.0008836852,0.00003354052,0.004315074,0.0002741668,0.8322369,0.0000978769],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3348571,0.0001619487,0.000170091,0.001113415,0.001871316,0.0003121446,0.5144269,0.00004958591,0.1470375],"genre_scores_gemma":[0.8176684,0.00008016287,0.0002468252,0.0006106396,0.00002698628,0.000006313886,0.1744559,0.000006263928,0.006898542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4828112,"threshold_uncertainty_score":0.9991285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06371622324894954,"score_gpt":0.313654097728842,"score_spread":0.2499378744798925,"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."}}