{"id":"W4398240957","doi":"10.1242/jeb.247167","title":"Integrating water balance mechanisms into predictions of insect responses to climate change","year":2024,"lang":"en","type":"article","venue":"Journal of Experimental Biology","topic":"Physiological and biochemical adaptations","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation","keywords":"Water balance; Climate change; Dehydration; Balance (ability); Trait; Vulnerability (computing); Biology; Environmental science; Phenotypic plasticity; Ecology; Adaptation (eye); Computer science","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":[0.001102853,0.0005516341,0.0004085,0.0003724857,0.0004605102,0.001648704,0.001221184,0.001385251,0.001402455],"category_scores_gemma":[0.003686164,0.0002156603,0.0004506941,0.0002083713,0.000926805,0.001698436,0.000543607,0.001449251,0.0004427806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259625,"about_ca_system_score_gemma":0.0007888377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006930252,"about_ca_topic_score_gemma":0.008917462,"domain_scores_codex":[0.9997932,0.00007280193,0.00001761958,0.00005721356,0.00003843159,0.0000207284],"domain_scores_gemma":[0.9993259,0.0003248901,0.00008721928,0.00003466195,0.0001750003,0.0000524118],"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":[0.0003052016,0.00006085437,0.02342874,0.001054997,0.0005940224,0.0005850082,0.0007583018,0.5392104,0.01785712,0.2542403,0.05121886,0.1106862],"study_design_scores_gemma":[0.00002197488,0.00008208456,0.0126799,0.0002679708,0.0001166025,0.00006056716,0.0004613513,0.5662391,0.003353703,0.3660166,0.05058056,0.0001196685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2318502,0.04281143,0.4629795,0.1769163,0.01697798,0.0001279159,0.002520627,0.002351895,0.06346425],"genre_scores_gemma":[0.9605395,0.01216652,0.01638414,0.003538847,0.003384538,0.0000571847,0.0002873203,0.000131416,0.003510387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006930252,"threshold_uncertainty_score":0.01377982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03382473403253458,"score_gpt":0.2975171273222331,"score_spread":0.2636923932896985,"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."}}