{"id":"W1104340590","doi":"10.1016/j.cbd.2015.07.004","title":"Predicting growth and mortality of bivalve larvae using gene expression and supervised machine learning","year":2015,"lang":"en","type":"article","venue":"Comparative Biochemistry and Physiology Part D Genomics and Proteomics","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Université du Québec à Rimouski","keywords":"Biology; Larva; Gene; Mytilus; Metamorphosis; Longevity; Eicosanoid; Arachidonic acid; Eicosapentaenoic acid; Gene expression; Zoology; Genetics; Ecology; Cell biology; Fatty acid; Biochemistry; Polyunsaturated fatty acid; Enzyme","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":[0.0001615672,0.0001607979,0.0002795169,0.000008056634,0.0002021772,0.00001496482,0.00004918335,0.00008047646,0.000006304228],"category_scores_gemma":[0.00002769352,0.0001318627,0.00002016725,0.00003559591,0.0005035755,0.00005451716,0.0004071018,0.0001399238,2.337703e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001080431,"about_ca_system_score_gemma":0.000008675026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001035319,"about_ca_topic_score_gemma":0.000005179995,"domain_scores_codex":[0.9992558,0.00006283688,0.0001737691,0.0003175458,0.00005322243,0.0001368647],"domain_scores_gemma":[0.9996467,0.00003454387,0.0001178824,0.0000760184,0.00001706875,0.0001077652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005797836,0.000013742,0.2594768,0.00004478584,0.0000253279,2.799757e-7,0.00103001,0.00005794734,0.7392518,0.000006261048,0.000007086334,0.00002802294],"study_design_scores_gemma":[0.0007685816,0.0001443586,0.07174798,0.00002751295,0.00005179739,0.0000159326,0.0006800738,0.0134482,0.9117137,0.0009765331,0.0001575064,0.0002678567],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984293,0.001063411,0.00005478663,0.00002009873,0.00001825517,0.000155367,0.00002911861,0.00000699836,0.0002226958],"genre_scores_gemma":[0.9969116,0.0004568107,0.002496843,0.00001934696,0.00005984454,0.000006863543,0.00002623164,0.000005090527,0.00001739242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1877288,"threshold_uncertainty_score":0.5377206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04849483012291535,"score_gpt":0.278047463916628,"score_spread":0.2295526337937127,"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."}}