{"id":"W4401548439","doi":"10.1016/j.dib.2024.110789","title":"QF-LCA dataset: Quantum Field Lens Coding Algorithm for system state simulation and strong predictions","year":2024,"lang":"en","type":"article","venue":"Data in Brief","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"University of Victoria","keywords":"Coding (social sciences); Algorithm; Computer science; Lens (geology); Field (mathematics); Quantum; Through-the-lens metering; State (computer science); Mathematics; Optics; Physics; Statistics; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001543634,0.003241961,0.001104912,0.001606584,0.0013778,0.001836327,0.00525817,0.003728488,0.01299833],"category_scores_gemma":[0.007515228,0.0007939765,0.002493164,0.001717123,0.0009147379,0.001574515,0.001631036,0.003335905,0.007594418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002665141,"about_ca_system_score_gemma":0.003300756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01950752,"about_ca_topic_score_gemma":0.03849422,"domain_scores_codex":[0.9989644,0.0001985725,0.00007419878,0.0003034648,0.0003274784,0.0001318494],"domain_scores_gemma":[0.9975733,0.001074423,0.0001132731,0.0005470437,0.0005260008,0.0001659568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008012019,0.0007588294,0.0110944,0.001967184,0.000493412,0.0004458639,0.0001564497,0.1680596,0.002924875,0.01408577,0.7586974,0.04051504],"study_design_scores_gemma":[0.0009797127,0.0003081361,0.004744755,0.0001765512,0.00009850594,0.0002251617,0.0001049561,0.8193157,0.007732713,0.02951558,0.1366935,0.0001047223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08517458,0.003119705,0.04009511,0.003079896,0.001039253,0.0007979474,0.7929314,0.05504824,0.01871392],"genre_scores_gemma":[0.09018075,0.0004297087,0.05660237,0.0007831259,0.00008937746,0.0009750226,0.845283,0.00189644,0.00376024],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01950752,"threshold_uncertainty_score":0.04348367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03746899776755722,"score_gpt":0.3072662405696709,"score_spread":0.2697972428021136,"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."}}