{"id":"W4233462064","doi":"10.1515/iupac.81.0488","title":"Interspecies Competition","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Ecosystem dynamics and resilience","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Ecotoxicology; Environmental risk assessment; Relation (database); Ecology; Computer science; Risk assessment; Biology; Data mining; Linguistics; Philosophy","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":[0.002344299,0.001686767,0.001781982,0.00372824,0.001622279,0.003935895,0.004215346,0.002191701,0.0993342],"category_scores_gemma":[0.0177134,0.0006437669,0.002504184,0.008570667,0.0006637768,0.003972743,0.003518015,0.002706803,0.0639485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002823302,"about_ca_system_score_gemma":0.003585157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03522751,"about_ca_topic_score_gemma":0.06783736,"domain_scores_codex":[0.9958577,0.0007721031,0.0004612103,0.001383952,0.0008906659,0.0006343729],"domain_scores_gemma":[0.9938599,0.002158433,0.0007511568,0.001554978,0.001193092,0.0004824401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001494734,0.00006035937,0.006795142,0.0009394921,0.0001056494,0.00006570738,0.00006864568,0.00103585,0.0001181412,0.005647435,0.9771867,0.007827515],"study_design_scores_gemma":[0.0002454173,0.00003588749,0.01156216,0.0005580654,0.00006051849,0.0002349219,0.0002295673,0.001724267,0.0002591392,0.01107004,0.9739571,0.00006286036],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001149291,0.000491698,0.0003522575,0.0003606282,0.0001133518,0.00003901675,0.9917178,0.0002904212,0.00548548],"genre_scores_gemma":[0.003395396,0.0002282255,0.0006827035,0.0002448913,0.00002610139,0.0001587244,0.9922712,0.0000880497,0.00290464],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0993342,"threshold_uncertainty_score":0.332306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006023449524543556,"score_gpt":0.3316085172621213,"score_spread":0.3255850677375777,"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."}}