{"id":"W4206708811","doi":"10.3389/fevo.2021.761984","title":"Inferring Size-Based Functional Responses From the Physical Properties of the Medium","year":2022,"lang":"en","type":"article","venue":"Frontiers in Ecology and Evolution","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Wilfrid Laurier University; University of Ottawa","funders":"Natural Resources Canada; Agence Nationale de la Recherche","keywords":"Functional response; Ecology; Relation (database); Computer science; Predation; Statistical physics; Predator; Physics; Biology; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001405785,0.0007328616,0.0004835646,0.0008565917,0.0002432925,0.0009426281,0.00096387,0.001051028,0.0009174306],"category_scores_gemma":[0.006837273,0.000458717,0.00108483,0.0003478873,0.0009982607,0.002552778,0.00112698,0.001169856,0.0003204608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007352459,"about_ca_system_score_gemma":0.0004107798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001680548,"about_ca_topic_score_gemma":0.001069416,"domain_scores_codex":[0.9996829,0.0001093706,0.00001872693,0.00009510118,0.00005651672,0.0000374733],"domain_scores_gemma":[0.9976754,0.001655342,0.0001776809,0.0002913642,0.0001373208,0.00006286697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002047528,0.0002559941,0.02730068,0.0008741808,0.0002103832,0.0006312255,0.0004855434,0.4467403,0.3772648,0.08552902,0.0008856818,0.0596175],"study_design_scores_gemma":[0.00001178174,0.00009200643,0.01251067,0.00002303144,0.00004386972,0.0001883706,0.00009520799,0.8937425,0.0295119,0.06288391,0.0008179886,0.00007867806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3796891,0.000395991,0.611707,0.0005146519,0.00007801304,0.00007482955,0.0002794315,0.0003999927,0.006861041],"genre_scores_gemma":[0.9671737,0.0003514922,0.03148515,0.0001002577,0.00001361567,0.00005722728,0.00008680525,0.00005683822,0.0006749716],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001680548,"threshold_uncertainty_score":0.007434607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01288149157386361,"score_gpt":0.1828869390698482,"score_spread":0.1700054474959846,"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."}}