{"id":"W1988162432","doi":"10.1016/j.tplants.2008.08.002","title":"Boosting tandem affinity purification of plant protein complexes","year":2008,"lang":"en","type":"article","venue":"Trends in Plant Science","topic":"Transgenic Plants and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":110,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada; Danmarks Grundforskningsfond; National Research Foundation","keywords":"Biology; Tandem affinity purification; Boosting (machine learning); Tandem; Computational biology; Plant biology; Plant science; Affinity chromatography; Botany; Biochemistry; Artificial intelligence; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008705591,0.0007893611,0.0007212837,0.0004595695,0.0003202189,0.001159764,0.0008822015,0.0006653477,0.00329989],"category_scores_gemma":[0.001130855,0.000444106,0.000411321,0.0005226827,0.0003542776,0.0007039721,0.001338776,0.001991267,0.003028146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006051675,"about_ca_system_score_gemma":0.000324876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000596258,"about_ca_topic_score_gemma":0.001060247,"domain_scores_codex":[0.9992467,0.0001106377,0.00003466072,0.0001556117,0.0002876748,0.0001647095],"domain_scores_gemma":[0.9993798,0.000172671,0.00005989896,0.00008482522,0.0001759543,0.0001268492],"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":[0.0001127258,0.00007900893,0.0002650753,0.00007161618,0.00001626475,0.00002088198,0.00002682361,0.0002972674,0.9886701,0.0004624719,0.0009467153,0.009031083],"study_design_scores_gemma":[0.00003314509,0.000115461,0.00107796,0.000009031254,0.00003792327,0.000239322,0.00002006555,0.007804801,0.9826011,0.0002949174,0.007753707,0.00001252245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8024253,0.005705182,0.1775181,0.001665917,0.0004010416,0.0003300442,0.0004388374,0.002459169,0.009056377],"genre_scores_gemma":[0.9209729,0.003451118,0.05809423,0.00123543,0.0002050023,0.0001890395,0.001804324,0.0006343836,0.01341366],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00329989,"threshold_uncertainty_score":0.01103926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04393770415534211,"score_gpt":0.2789366397365504,"score_spread":0.2349989355812083,"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."}}