{"id":"W2125731809","doi":"10.1002/ieam.129","title":"Toward a knowledge infrastructure for traits-based ecological risk assessment","year":2010,"lang":"en","type":"article","venue":"Integrated Environmental Assessment and Management","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact; Environment and Climate Change Canada; University of Guelph; University of New Brunswick","funders":"","keywords":"Trait; Data science; Semantic Web; Metadata; Leverage (statistics); Computer science; Construct (python library); Process (computing); Field (mathematics); Knowledge management; World Wide Web","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":[0.01702793,0.0008743415,0.001382129,0.01353461,0.002061453,0.011078,0.005297692,0.003270342,0.005148552],"category_scores_gemma":[0.02950956,0.000984105,0.002264847,0.01227584,0.00297116,0.02185596,0.007205321,0.003865629,0.003979838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003374434,"about_ca_system_score_gemma":0.009197934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01034488,"about_ca_topic_score_gemma":0.007040869,"domain_scores_codex":[0.9942825,0.001752127,0.001309072,0.001102271,0.001257423,0.000296603],"domain_scores_gemma":[0.9740966,0.009744207,0.002381744,0.007717825,0.004751326,0.001308335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001303354,0.0004784314,0.008414156,0.001134176,0.0002636855,0.0007266101,0.002465451,0.02304319,0.005269051,0.5061995,0.02817984,0.4236955],"study_design_scores_gemma":[0.00004787139,0.00008102072,0.004685629,0.00126276,0.000245695,0.0005254654,0.001839535,0.08792848,0.005352136,0.6264184,0.2714118,0.0002011179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005174317,0.0007452636,0.9655855,0.007790634,0.00009116259,0.0002543214,0.002480386,0.006407509,0.01147084],"genre_scores_gemma":[0.05175976,0.001736927,0.9331305,0.0008113005,0.0001673701,0.0003669961,0.009376888,0.0005023025,0.002147881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01702793,"threshold_uncertainty_score":0.09005338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444475306432171,"score_gpt":0.2769367135565196,"score_spread":0.2624919604921979,"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."}}