{"id":"W2903869689","doi":"10.1101/502146","title":"Seed masting causes fluctuations in optimum litter size and lag load in a seed predator","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Animal Ecology and Behavior Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Alberta; University of Guelph","funders":"Ontario Ministry of Research and Innovation; Polar Knowledge Canada; National Science Foundation","keywords":"Predation; Mast (botany); Biology; Predator; Maladaptation; Litter; Ecology; Seed predation; Biological dispersal; Population; Seed dispersal; Mast cell","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":[0.0001441753,0.0001308566,0.0001204058,0.0002045481,0.0001707316,0.0001677455,0.0001080179,0.0001343941,0.0006271238],"category_scores_gemma":[0.0002621891,0.0001410133,0.0001492388,0.00007589528,0.0001396334,0.00008348871,0.0001338262,0.0001931095,0.00007617806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002250901,"about_ca_system_score_gemma":0.00007153939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001797485,"about_ca_topic_score_gemma":0.006760918,"domain_scores_codex":[0.999933,0.00001063483,0.000004717187,0.00002633036,0.00001204112,0.00001326566],"domain_scores_gemma":[0.99966,0.00005482889,0.0001542373,0.00002725903,0.00003044386,0.00007334776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004370695,0.0001460084,0.6032264,0.00002292418,0.00006787995,0.0002476559,0.0002336282,0.0002500961,0.3907828,0.00002426322,0.0001052762,0.004455922],"study_design_scores_gemma":[0.00000197287,0.0001211465,0.9968747,7.635585e-7,0.000007692703,0.00007474523,0.00003418433,0.0002397609,0.002590124,0.000005748802,0.00004768754,0.000001435291],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9999011,0.00001024655,0.00002583315,0.000001940387,3.967417e-7,6.953808e-7,0.00001475656,0.000002004479,0.00004303757],"genre_scores_gemma":[0.9997675,0.000008812249,0.0000652845,0.000006976609,8.828462e-7,0.000001933748,0.00004639012,9.333824e-7,0.0001012683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001797485,"threshold_uncertainty_score":0.003574073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383095531794654,"score_gpt":0.227768179425826,"score_spread":0.2139372241078795,"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."}}