{"id":"W7095130346","doi":"","title":"Considerations on yield, nutrient uptake, cellular growth, and competition in chemostat models, Canadian Applied Mathematics Quarterly 11(2","year":2003,"lang":"en","type":"article","venue":"","topic":"Mathematical and Theoretical Epidemiology and Ecology Models","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chemostat; Constant (computer programming); Yield (engineering); Competition (biology); Stability (learning theory); Function (biology); Substrate (aquarium); Competition model","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002099935,0.0009246027,0.001139728,0.001174367,0.001413741,0.002926443,0.001171412,0.002300672,0.004820167],"category_scores_gemma":[0.007491929,0.0004916494,0.0008346686,0.001139535,0.003582557,0.004868914,0.001846527,0.001639175,0.0003838851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004658686,"about_ca_system_score_gemma":0.002247338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0203969,"about_ca_topic_score_gemma":0.01892147,"domain_scores_codex":[0.9993542,0.0003574716,0.00002868592,0.00005523496,0.0001386031,0.0000659057],"domain_scores_gemma":[0.9966102,0.002277908,0.0004321305,0.0001349221,0.0003455281,0.0001993052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001217325,0.00001240348,0.0008970799,0.00005455425,0.00001625501,0.0001277465,0.0001427626,0.06488641,0.0004451247,0.9295459,0.001651472,0.002208136],"study_design_scores_gemma":[0.000006572076,0.00003109429,0.0007792498,0.00003516855,0.00001393707,0.00006696923,0.00009031225,0.2950121,0.0001550616,0.6986183,0.005174631,0.00001678869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2993326,0.0395693,0.4398869,0.04826743,0.0007353546,0.00008751956,0.0005639477,0.000203033,0.171354],"genre_scores_gemma":[0.9719599,0.004731504,0.01068401,0.0004915341,0.0003986994,0.00007216678,0.00006875001,0.00005063886,0.01154277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0203969,"threshold_uncertainty_score":0.04055631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03440331791405848,"score_gpt":0.2386047959638059,"score_spread":0.2042014780497474,"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."}}