{"id":"W4414627451","doi":"10.18154/rwth-2026-05013","title":"Euclid preparation LXXVIII - Full-shape modelling of two-point and three-point correlation functions in real space","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Fundação para a Ciência e a Tecnologia; Norsk Romsenter; Agenția Spațială Română; National Astronomical Observatory of Japan; Agenzia Spaziale Italiana; Magyar Tudományos Akadémia; Ministero dell’Istruzione, dell’Università e della Ricerca; European Space Agency; National Aeronautics and Space Administration","keywords":"Scalar (mathematics); Range (aeronautics); Spectral density; Scale (ratio); Consistency (knowledge bases); Correlation function (quantum field theory); Amplitude; Population; Space (punctuation)","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":[0.001481201,0.0005456026,0.0006130444,0.0004694542,0.0003989719,0.0008627689,0.001653148,0.0008391292,0.0039739],"category_scores_gemma":[0.003124905,0.0003389636,0.001032744,0.000512647,0.0005400371,0.0007615631,0.0007725974,0.0007495579,0.00120485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008673882,"about_ca_system_score_gemma":0.0008964425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009121107,"about_ca_topic_score_gemma":0.005059871,"domain_scores_codex":[0.9996033,0.0001616076,0.00001383698,0.00006556583,0.00009834068,0.00005733169],"domain_scores_gemma":[0.9987448,0.0004918232,0.0001555635,0.0003480802,0.0001811818,0.00007842123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006225156,0.00003591769,0.003862903,0.00004536587,0.00004227665,0.0001474147,0.00006490557,0.9474558,0.002030784,0.03831923,0.002652616,0.005280463],"study_design_scores_gemma":[0.000007981096,0.00001336278,0.0009606105,0.000007485644,0.000003023156,0.00003144138,0.000008548907,0.9913291,0.0009245031,0.005526571,0.001177391,0.000009973661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3571035,0.0003202137,0.6015624,0.0005551183,0.00008713353,0.0001229694,0.006465699,0.004522026,0.02926104],"genre_scores_gemma":[0.8737869,0.0001701001,0.1162713,0.0001578955,0.0000436824,0.0002663924,0.004067369,0.001379186,0.003857246],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009121107,"threshold_uncertainty_score":0.01813608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04196422927014749,"score_gpt":0.2613933843074788,"score_spread":0.2194291550373313,"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."}}