{"id":"W4200158234","doi":"10.1111/2041-210x.13795","title":"Accounting for a nonlinear functional response when estimating prey dynamics using predator diet data","year":2021,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Ocean Frontier Institute","keywords":"Predation; Trophic level; Functional response; Predator; Abundance (ecology); Population; Forage fish; Forage; Food web; Ecology; Population cycle; Apex predator; Biology; Environmental science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003198828,0.00006901769,0.0001177074,0.00003557961,0.0002111499,0.00002525738,0.0001259785,0.0001110945,0.000507342],"category_scores_gemma":[0.003450491,0.00007323261,0.00001512667,0.0001406663,0.0001214267,0.0003226543,0.0007818607,0.0001548547,0.000002462472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002639703,"about_ca_system_score_gemma":0.00006832195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001035911,"about_ca_topic_score_gemma":0.001458132,"domain_scores_codex":[0.9986542,0.000514024,0.000183161,0.0003429958,0.00007334255,0.0002322877],"domain_scores_gemma":[0.9988689,0.0007886874,0.00005257676,0.0002378497,0.00001914761,0.00003281442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007950547,0.00006870316,0.9667267,0.00003207168,0.00001199106,0.000004178904,0.0001034085,0.001718344,0.001957324,0.00009572563,0.0002380948,0.02824839],"study_design_scores_gemma":[0.0002214961,0.00004898393,0.225694,0.000003338404,0.000008198824,0.00001443572,0.0001000658,0.7694702,0.00001850045,0.002743731,0.001616538,0.00006053308],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4074227,0.00001205223,0.591408,0.0002209774,0.0002023641,0.000144344,0.00003458369,0.00001010078,0.0005448814],"genre_scores_gemma":[0.01397309,0.000002929019,0.9853722,0.00005689263,0.00006504399,0.00002237972,0.0001195908,0.000007849578,0.0003799467],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7677519,"threshold_uncertainty_score":0.555504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07891719014876422,"score_gpt":0.3897240451980808,"score_spread":0.3108068550493166,"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."}}