{"id":"W6926396820","doi":"10.25345/c5mg7g54j","title":"MassIVE MSV000091174 - Human Rhomboid4 BioID LC-MSMS proximity interactomics data","year":2023,"lang":"te","type":"dataset","venue":"UC San Diego","topic":"Canadian Identity and History","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Feature (linguistics); Pattern recognition (psychology); Identification (biology); Stability (learning theory)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001028793,0.002466486,0.001839839,0.00458145,0.002158241,0.002474674,0.002752325,0.002726787,0.05589219],"category_scores_gemma":[0.004273555,0.0008482391,0.001756994,0.006061273,0.0006941801,0.0008152631,0.002583262,0.001801839,0.05411262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001759974,"about_ca_system_score_gemma":0.005911156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07594831,"about_ca_topic_score_gemma":0.1605372,"domain_scores_codex":[0.9989225,0.0001076245,0.00006463172,0.000337768,0.0003304638,0.000236976],"domain_scores_gemma":[0.9983859,0.0003925011,0.0001270628,0.0003877794,0.0003728932,0.0003337952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000292027,0.0000405663,0.002124418,0.0009532229,0.0001205645,0.0001011055,0.00009022905,0.0005707761,0.002190122,0.001200947,0.9887201,0.003595916],"study_design_scores_gemma":[0.000610105,0.00005537553,0.01300092,0.0003774563,0.0002157658,0.000239603,0.0002074545,0.001399162,0.002994183,0.003612361,0.9771946,0.00009298205],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006322213,0.0001503971,0.0001672974,0.00007088705,0.00002833607,0.00001348536,0.997118,0.0008904831,0.0009288839],"genre_scores_gemma":[0.001074237,0.00008489208,0.0005732452,0.00006779317,0.000007343719,0.00005894946,0.9973168,0.0001327373,0.0006839116],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07594831,"threshold_uncertainty_score":0.186978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06001917048961284,"score_gpt":0.3191229943776502,"score_spread":0.2591038238880374,"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."}}