{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003241799,0.001183541,0.0006856472,0.001829313,0.001893709,0.006103799,0.001409439,0.002237623,0.892769],"category_scores_gemma":[0.001921852,0.0005429964,0.0008388766,0.001714139,0.0005613383,0.004690734,0.002391609,0.001923112,0.8259587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001337887,"about_ca_system_score_gemma":0.001383905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005434863,"about_ca_topic_score_gemma":0.00934419,"domain_scores_codex":[0.9992378,0.00003139626,0.00002498149,0.0001483127,0.0004930207,0.00006446937],"domain_scores_gemma":[0.9987769,0.0000964934,0.00003849617,0.0001691305,0.0005917858,0.000327342],"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":[0.00005416059,0.00005367181,0.0001549294,0.0001326933,0.000005044898,0.0001069432,0.00004143056,0.00006971409,0.0007324709,0.004239878,0.9137131,0.08069611],"study_design_scores_gemma":[0.000007407027,0.00001357297,0.0002219541,0.00002912068,0.000002401228,0.00007833754,0.00003567304,0.00004758808,0.0002375753,0.0005067005,0.9988137,0.000005838495],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"dataset","genre_scores_codex":[0.0006664579,0.0006460092,0.0006160763,0.001475198,0.001987069,0.0000697484,0.002667356,0.003158724,0.9887134],"genre_scores_gemma":[0.001969222,0.0004444145,0.0003341089,0.0005297696,0.0003083717,0.00001608721,0.001295269,0.0008454499,0.9942573],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.107231,"threshold_uncertainty_score":0.152952,"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."}}