{"id":"W1993758830","doi":"10.1002/ece3.656","title":"Modeling implications of food resource aggregation on animal migration phenology","year":2013,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut du Savoir Montfort","funders":"Office of Polar Programs; Fisheries and Oceans Canada; Agence Nationale de la Recherche","keywords":"Phenology; Ecology; Beluga Whale; Spatial distribution; Resource (disambiguation); Arctic; Environmental science; Abundance (ecology); Beluga; Biology; Geography","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":[0.001098127,0.0004777036,0.0006305348,0.000507349,0.0004453012,0.0009546326,0.0008845035,0.001353306,0.001168837],"category_scores_gemma":[0.003729978,0.0004048587,0.0009411371,0.0005909278,0.0005660774,0.000686665,0.0006977118,0.0006708346,0.0001217951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009760374,"about_ca_system_score_gemma":0.0007643153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04271874,"about_ca_topic_score_gemma":0.01963733,"domain_scores_codex":[0.9997255,0.000154029,0.000008340531,0.00004733388,0.00001406658,0.00005073043],"domain_scores_gemma":[0.9977324,0.001763085,0.0002143937,0.00009179275,0.00007750571,0.0001207489],"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":[0.00002866498,0.0000179483,0.00820454,0.00001188695,0.00003327632,0.00002771971,0.0000230555,0.9893622,0.0003155406,0.0008514441,0.00009415329,0.001029624],"study_design_scores_gemma":[0.000007576388,0.00001801962,0.002132594,0.000003305876,0.00001275468,0.000007268222,0.00001230553,0.9971147,0.00004634586,0.0005551311,0.000085985,0.00000409627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731928,0.0002524086,0.02304595,0.0004449653,0.00003933201,0.00001791774,0.0003095098,0.00008336327,0.002613772],"genre_scores_gemma":[0.9963832,0.0001035319,0.00264102,0.00004171214,0.00001840008,0.00002363779,0.00007899368,0.0000168916,0.0006926192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04271874,"threshold_uncertainty_score":0.0849402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01619435548372456,"score_gpt":0.2197923582140499,"score_spread":0.2035980027303254,"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."}}