{"id":"W4210535526","doi":"10.3897/neobiota.71.75711","title":"Predatory ability and abundance forecast the ecological impacts of two aquatic invasive species","year":2022,"lang":"en","type":"article","venue":"NeoBiota","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; University of Windsor","funders":"Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; University of Windsor","keywords":"Brown trout; Salmo; Biology; Predation; Ecology; Carcinus maenas; Predator; Abundance (ecology); Interspecific competition; Apex predator; Intraguild predation; Trophic level; Invasive species; Introduced species; Fishery; Vital rates; Catch per unit effort; Benthic zone; Population; Crustacean; Decapoda; Population growth; Fish <Actinopterygii>","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003608816,0.00007240622,0.0001128154,0.000009784664,0.0003823623,0.000006590583,0.0002011728,0.00001629159,0.003428835],"category_scores_gemma":[0.0001243031,0.0000483695,0.00002620489,0.00009870026,0.0006720637,0.00006029769,0.0009835305,0.0001123346,0.00002350485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008568658,"about_ca_system_score_gemma":0.000006829391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009687488,"about_ca_topic_score_gemma":0.003090403,"domain_scores_codex":[0.9993137,0.0001062384,0.0001189379,0.0001829338,0.0001248813,0.0001533168],"domain_scores_gemma":[0.9994643,0.0002563538,0.00007395443,0.000180723,0.000002109058,0.00002255524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002180885,0.0001328495,0.9644562,0.000015566,0.00002644199,0.000004338633,0.0009336675,0.0002173284,0.001269647,0.0006176333,0.0319379,0.000366649],"study_design_scores_gemma":[0.0002017503,0.0001815647,0.9883902,0.000001208812,0.00001350725,0.000003059289,0.000600411,0.0001016039,0.0001580997,0.002333879,0.007958603,0.00005607651],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844409,0.00004073416,0.000005661146,0.001517756,0.00008872494,0.0002807319,0.0000121442,0.00001072104,0.01360269],"genre_scores_gemma":[0.9983444,0.00004008779,0.00007060277,0.0008632466,0.000009587974,0.00004117604,0.000001496039,0.000002720611,0.0006267056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0239793,"threshold_uncertainty_score":0.9974822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01403792730318411,"score_gpt":0.2196293499532866,"score_spread":0.2055914226501024,"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."}}