{"id":"W6931659582","doi":"10.5281/zenodo.7840018","title":"IPBES Invasive Alien Species Assessment: data management report of chapter 6. Supplementary material 6.3 Table of knowledge and data gaps","year":2023,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Information and Cyber Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Table (database); Alien species; Alien; Introduced species; Invasive species; Biodiversity","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":[0.009616212,0.001285156,0.0009867479,0.009825619,0.0009170085,0.003548733,0.002318993,0.001377117,0.1801134],"category_scores_gemma":[0.03312845,0.0009493731,0.001087118,0.009931218,0.0004914953,0.002577895,0.002233894,0.002208391,0.1161213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002277635,"about_ca_system_score_gemma":0.009121046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01752541,"about_ca_topic_score_gemma":0.01414996,"domain_scores_codex":[0.9935708,0.001061824,0.0008831052,0.0006731403,0.003551456,0.0002595972],"domain_scores_gemma":[0.9706587,0.008664257,0.002452405,0.002706195,0.01471809,0.00080041],"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":[0.0001084132,0.00004402181,0.001390978,0.00157421,0.00004580042,0.00006725808,0.00008124558,0.00113168,0.0006033569,0.003231758,0.9591237,0.03259752],"study_design_scores_gemma":[0.00003890589,0.00003363003,0.003748261,0.0009984407,0.00003543892,0.00009796858,0.0001186403,0.0004658622,0.001221398,0.003245038,0.9899541,0.00004221278],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0006068228,0.0004347581,0.007993136,0.0008859191,0.0003545189,0.0007550548,0.9540066,0.00208103,0.03288216],"genre_scores_gemma":[0.003700515,0.001232212,0.02755337,0.0005812171,0.000142808,0.003070431,0.9420021,0.001625318,0.02009203],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1801134,"threshold_uncertainty_score":0.6025392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0606468670955146,"score_gpt":0.2910156551538755,"score_spread":0.2303687880583609,"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."}}