{"id":"W6911160365","doi":"10.5061/dryad.gm3vr7s","title":"Data from: Ecological specialization in populations adapted to constant versus heterogeneous environments","year":2019,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Animal Behavior and Reproduction","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Constant (computer programming); Adaptation (eye); Variation (astronomy); Juvenile; Population; Local adaptation; Selection (genetic algorithm)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002006552,0.00215546,0.001689898,0.003189099,0.0008712649,0.002430829,0.00300651,0.002540694,0.05590777],"category_scores_gemma":[0.01005818,0.0008111541,0.001270284,0.00507255,0.0005763444,0.001230545,0.002426585,0.001940336,0.04127562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001359605,"about_ca_system_score_gemma":0.00237683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01770808,"about_ca_topic_score_gemma":0.02462709,"domain_scores_codex":[0.9987552,0.0002197358,0.0002460545,0.0003764452,0.0002415449,0.000160977],"domain_scores_gemma":[0.9966111,0.001465368,0.0004566075,0.0006134098,0.000572401,0.0002812115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0002351405,0.0000635114,0.004385828,0.006395444,0.0002459821,0.00009555281,0.0001520505,0.001011748,0.0008468434,0.00130362,0.9797164,0.005547862],"study_design_scores_gemma":[0.001041929,0.00003688192,0.01664823,0.0009766438,0.0001578127,0.0001171048,0.0001497428,0.0009955645,0.000885636,0.002411815,0.9764991,0.00007968767],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002620425,0.00007480607,0.0000793752,0.00005448089,0.00001465146,0.0000121776,0.9990085,0.0002286308,0.0002653223],"genre_scores_gemma":[0.0009761302,0.00009550699,0.0004829716,0.00004176332,0.000005005817,0.0001484109,0.9978892,0.00006671024,0.0002943413],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05590777,"threshold_uncertainty_score":0.1870301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1244156529184971,"score_gpt":0.3000867544355092,"score_spread":0.1756711015170121,"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."}}