{"id":"W6889050690","doi":"10.25345/c5w41b","title":"MassIVE MSV000085066 - Cao_detergent insoluble pellets_Cortex_proteomics","year":2020,"lang":"en","type":"dataset","venue":"UC San Diego","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Process (computing); Yield (engineering); Identification (biology); Work (physics)","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","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0002911224,0.001449133,0.00151555,0.00056954,0.0003073842,0.0003069595,0.002273297,0.0009973139,0.01617928],"category_scores_gemma":[0.000520056,0.001521138,0.0006229741,0.0008276642,0.0002798722,0.0002322632,0.001472401,0.002075509,0.2235015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007360085,"about_ca_system_score_gemma":0.0006576456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005255806,"about_ca_topic_score_gemma":0.001432147,"domain_scores_codex":[0.9938312,0.0003317352,0.001203627,0.001909439,0.001342276,0.001381728],"domain_scores_gemma":[0.9950801,0.0001344303,0.001064431,0.002618113,0.0002184082,0.0008845796],"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":[0.0002370537,0.0002151993,0.00002047951,0.0003273423,0.0003966944,0.001131309,0.00008122155,0.00003076638,0.0004700528,0.00002012617,0.9968534,0.0002164239],"study_design_scores_gemma":[0.0009972556,0.0003104676,0.00004935685,0.0001737716,0.0004561823,0.00004151607,0.00006216628,0.00008980226,0.0005950496,0.0001357971,0.995477,0.001611655],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002105383,0.0005706866,0.00002016199,0.0001447666,0.002217323,0.00176317,0.9936572,0.0004568198,0.0009592801],"genre_scores_gemma":[0.000118897,0.0003427678,0.0005775391,0.001186949,0.001491163,0.0003098594,0.9946625,0.0004797326,0.000830617],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2073222,"threshold_uncertainty_score":0.9998258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02421103595776922,"score_gpt":0.2575287593162269,"score_spread":0.2333177233584577,"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."}}