{"id":"W2010699022","doi":"10.1002/pmic.200300588","title":"Further advances in the development of a data interchange standard for proteomics data","year":2003,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Publication; Proteomics; Data science; Computer science; Library science; Political science; Chemistry; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.13212,0.002739131,0.002614467,0.00868287,0.002594681,0.01330339,0.009931996,0.006494841,0.01065776],"category_scores_gemma":[0.12519,0.001962012,0.005384922,0.009606076,0.004382859,0.03113639,0.007416833,0.01640616,0.01065665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006398616,"about_ca_system_score_gemma":0.01689677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007857677,"about_ca_topic_score_gemma":0.00240012,"domain_scores_codex":[0.959461,0.01443236,0.007614429,0.003526964,0.01341661,0.001548649],"domain_scores_gemma":[0.8231538,0.03157383,0.004976251,0.03910186,0.09652614,0.004668144],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004154655,0.001058489,0.002936825,0.001899066,0.0002344863,0.0003803494,0.0021231,0.003758028,0.01712846,0.3638792,0.07779182,0.5283946],"study_design_scores_gemma":[0.0002079807,0.0004614612,0.00177028,0.001939628,0.0001694185,0.0008743506,0.0007489262,0.02055257,0.02143812,0.0960153,0.8555275,0.0002944998],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006514621,0.01184102,0.9020817,0.04674637,0.006160762,0.001647926,0.001232727,0.00353883,0.02023608],"genre_scores_gemma":[0.01698609,0.01094269,0.9500648,0.005021513,0.00204371,0.0007970763,0.005884578,0.001021654,0.007237751],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.86788,"threshold_uncertainty_score":0.6987255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1020155716175446,"score_gpt":0.3718554415062255,"score_spread":0.2698398698886809,"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."}}