{"id":"W4405664230","doi":"10.1101/2024.12.18.629253","title":"Defining the molecular impacts of Humalite application on field-grown wheat ( <i>Triticum aestivum</i> L.) using quantitative proteomics","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Phytase and its Applications","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Proteomics; Agronomy; Field (mathematics); Quantitative proteomics; Biology; Mathematics; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000198507,0.0002962064,0.0002658021,0.0001944281,0.0001906077,0.0004159799,0.0001726198,0.0003460132,0.0007179696],"category_scores_gemma":[0.0001351171,0.0001312087,0.000250371,0.0002083931,0.0002231534,0.0002476958,0.0001624167,0.0003889891,0.0001638792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003927356,"about_ca_system_score_gemma":0.0001693663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002553753,"about_ca_topic_score_gemma":0.002962836,"domain_scores_codex":[0.9998596,0.00001650142,0.00000896596,0.00005393045,0.00003618023,0.00002477087],"domain_scores_gemma":[0.9999044,0.00002157435,0.00003150055,0.000008288133,0.00001998481,0.00001420012],"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":[0.00004022828,0.000006880593,0.0002224007,0.00001951653,0.00000333826,0.000006704662,0.000007539109,0.00002098341,0.9992782,0.000008429613,0.00001609568,0.0003696813],"study_design_scores_gemma":[0.000008822377,0.000185195,0.03527705,0.000005572434,0.00002275164,0.00005712778,0.00007638529,0.00149065,0.9617225,0.00003855974,0.001106938,0.000008494259],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924192,0.0009015372,0.003406662,0.0001126692,0.00003573182,0.00003778693,0.001996191,0.0001358102,0.000954386],"genre_scores_gemma":[0.9894083,0.0008149728,0.004829595,0.0002219283,0.00001192443,0.0000720153,0.001860321,0.00004768017,0.002733244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002553753,"threshold_uncertainty_score":0.005077779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01625351354493544,"score_gpt":0.247734564292203,"score_spread":0.2314810507472675,"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."}}