{"id":"W2050531191","doi":"10.1111/j.1420-9101.2004.00772.x","title":"The effect of temperature and wing morphology on quantitative genetic variation in the cricket <i>Gryllus firmus</i>, with an appendix examining the statistical properties of the Jackknife–manova method of matrix comparison","year":2004,"lang":"en","type":"article","venue":"Journal of Evolutionary Biology","topic":"Orthoptera Research and Taxonomy","field":"Agricultural and Biological Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jackknife resampling; Wing; Biology; Multivariate analysis of variance; Quantitative genetics; Evolutionary biology; Variation (astronomy); Cricket; Trait; Quantitative trait locus; Principal component analysis; Genetic variation; Zoology; Statistics; Genetics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.001305606,0.00009528409,0.0002547494,0.00002449867,0.0001853268,0.00001220892,0.000315305,0.00006926797,0.0000120881],"category_scores_gemma":[0.0002642485,0.00002025058,0.00004502146,0.0002203239,0.0004993015,0.00005904014,0.00004721943,0.0003127457,4.729593e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001994789,"about_ca_system_score_gemma":0.00004398138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001922347,"about_ca_topic_score_gemma":0.0001647969,"domain_scores_codex":[0.9974651,0.001690479,0.0003859554,0.0001116715,0.0001900911,0.0001566909],"domain_scores_gemma":[0.9975044,0.001883204,0.0003985177,0.00007060618,0.000118984,0.00002428876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.005689953,0.0004447112,0.3254865,0.00007508648,0.0002373844,0.00001840867,0.002034461,0.003590388,0.6340619,0.01164129,0.0001550669,0.01656482],"study_design_scores_gemma":[0.0005150736,0.01033597,0.9837037,0.00007416627,0.00003171931,0.0001816637,0.001309893,0.0002839538,0.002475146,0.000843054,0.000191659,0.00005397645],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968581,0.001291864,0.0002063727,0.001278099,0.00004341288,0.0002741077,0.00002514481,0.000001149674,0.00002170664],"genre_scores_gemma":[0.9978325,0.00004586279,0.002027352,0.00002692064,0.00005318252,0.000006642447,0.000004263661,7.818513e-7,0.00000246976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6582172,"threshold_uncertainty_score":0.1839697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03369296188906084,"score_gpt":0.2996808448789147,"score_spread":0.2659878829898538,"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."}}