{"id":"W6949573180","doi":"10.5281/zenodo.15490267","title":"Red Mercury Price","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Bottle; Red mud; Liquid liquid; Red meat","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.001298025,0.0003884567,0.000411691,0.0006338547,0.002056979,0.002281888,0.008257982,0.0002579772,0.005430187],"category_scores_gemma":[0.001556219,0.0004204448,0.0001441557,0.001821634,0.0001861642,0.0004328903,0.01177794,0.0009086472,0.004010526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004155437,"about_ca_system_score_gemma":0.00003011901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004632882,"about_ca_topic_score_gemma":6.037819e-7,"domain_scores_codex":[0.9957427,0.0008508908,0.000532019,0.00133582,0.0008595343,0.0006790307],"domain_scores_gemma":[0.9955716,0.0001153468,0.0002632464,0.002786684,0.0009846898,0.0002784409],"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":[0.00000942604,0.000178699,5.669557e-8,0.0001675549,0.00004789265,0.00003888119,0.00009050502,0.00003279834,0.00002151714,0.00107227,0.9508457,0.04749466],"study_design_scores_gemma":[0.0002841711,0.0001625909,0.00003147261,0.00006419013,0.00002090888,0.0001013491,0.00001291844,0.001567475,0.00002072424,0.000565754,0.9967741,0.0003943873],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000005711652,0.000214808,0.09375416,0.002562547,0.001127887,0.001083564,0.8664899,0.002414666,0.03234679],"genre_scores_gemma":[0.0000520989,0.0002275052,0.004101824,0.0004238328,0.0003436839,1.060739e-7,0.9933349,0.0005020043,0.001014029],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.126845,"threshold_uncertainty_score":0.9998248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02686380525080776,"score_gpt":0.2539831923012131,"score_spread":0.2271193870504053,"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."}}