{"id":"W2952245216","doi":"10.1098/rspb.2018.1076","title":"Nonlinear averaging of thermal experience predicts population growth rates in a thermally variable environment","year":2018,"lang":"en","type":"article","venue":"Proceedings of the Royal Society B Biological Sciences","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Population; Population growth; Constant (computer programming); Persistence (discontinuity); Context (archaeology); Growth rate; Thermal; Nonlinear system; Environmental science; Population size; Variable (mathematics); Atmospheric sciences; Mathematics; Biology; Thermodynamics; Physics; Computer science; Demography; Geology","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":[0.0006993558,0.0002413002,0.0001984459,0.0002977137,0.000204893,0.000472036,0.0003086348,0.0002285119,0.0003942932],"category_scores_gemma":[0.003266053,0.0001613965,0.0003309727,0.0002014718,0.0005657894,0.0007028617,0.0003317388,0.0003383312,0.000138557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006771707,"about_ca_system_score_gemma":0.0003562105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00758746,"about_ca_topic_score_gemma":0.005881584,"domain_scores_codex":[0.9998471,0.00004126328,0.000009641589,0.00005254241,0.00002458146,0.0000248982],"domain_scores_gemma":[0.9989329,0.0005245344,0.0002378792,0.0001560695,0.00009998009,0.00004848791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001306172,0.00004508929,0.180687,0.00005175734,0.0001022742,0.0000881415,0.0002906117,0.7476109,0.04557021,0.005373998,0.000229187,0.01982024],"study_design_scores_gemma":[0.00000178203,0.00003131014,0.05302004,0.000002095339,0.0000127513,0.00002837608,0.00003101039,0.941274,0.002514375,0.00299022,0.00008023861,0.00001372627],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9585705,0.00002879407,0.04016173,0.00005025618,0.000003535898,0.000004782218,0.00005542724,0.00005755117,0.001067416],"genre_scores_gemma":[0.9984484,0.00001637875,0.001407493,0.000003745964,0.000001550568,0.000003582977,0.00003529008,0.000007267374,0.00007630701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00758746,"threshold_uncertainty_score":0.01508659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179605398965982,"score_gpt":0.2247519191967993,"score_spread":0.2067913793002011,"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."}}