{"id":"W2040455265","doi":"10.1038/nmat3926","title":"Boosting plant biology","year":2014,"lang":"en","type":"letter","venue":"Nature Materials","topic":"Photosynthetic Processes and Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Boosting (machine learning); Plant biology; Nanotechnology; Materials science; Computational biology; Biology; Computer science; Artificial intelligence; Botany","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0002605329,0.000352058,0.0004226221,0.00005394665,0.00006516172,0.00005474836,0.0004444295,0.003680394,0.0001738521],"category_scores_gemma":[0.0001934358,0.0002908812,0.00009147358,0.00002915522,0.00004568383,0.000001085601,0.0001783071,0.000726173,0.00004855117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001024658,"about_ca_system_score_gemma":0.00007270735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001128109,"about_ca_topic_score_gemma":0.000001708596,"domain_scores_codex":[0.9984525,0.0001140492,0.0002782659,0.0005961726,0.0001198555,0.0004391456],"domain_scores_gemma":[0.9991317,0.00002319338,0.0002397863,0.0004950692,0.00006942178,0.00004083291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001613975,0.000003357876,3.852456e-7,0.0001011541,0.00003050484,0.00001668877,0.000001901602,4.034242e-8,0.6391469,0.00008619583,0.3605438,0.0000530024],"study_design_scores_gemma":[0.00008535955,0.00006845014,2.543167e-7,0.00003460113,0.00001896785,0.00006736467,6.1382e-7,1.715674e-7,0.4944883,0.0004759351,0.5045743,0.0001856702],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.207718,0.0076232,0.004267213,0.7014806,0.03644035,0.003231721,0.01226697,0.0005422843,0.02642967],"genre_scores_gemma":[0.1858442,0.0001874384,0.0003429951,0.7877755,0.01432535,0.00006384069,0.009895088,0.0001095798,0.001456008],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.1446586,"threshold_uncertainty_score":0.9999543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007562956720652013,"score_gpt":0.2452964769136992,"score_spread":0.2377335201930472,"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."}}