{"id":"W4289375650","doi":"10.4230/lipics.itcs.2025.20","title":"Estimating Euclidean Distance to Linearity","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Variation (astronomy); Upper and lower bounds; Mathematics; Constant (computer programming); Total variation; Statistics; Combinatorics; Physics; Computer science; Mathematical analysis; Astrophysics","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"],"consensus_categories":[],"category_scores_codex":[0.0005389344,0.0003141473,0.0004753311,0.00009994212,0.0001551506,0.00007381848,0.0006842857,0.0002460675,0.0003443586],"category_scores_gemma":[0.003185783,0.0003401072,0.0001358915,0.0002985163,0.0001595607,0.00005389768,0.001095266,0.0005639553,0.000204928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001563019,"about_ca_system_score_gemma":0.00009870004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009441453,"about_ca_topic_score_gemma":0.00006086535,"domain_scores_codex":[0.9981852,0.0001880361,0.0002950792,0.0008607414,0.0001006615,0.0003703483],"domain_scores_gemma":[0.997329,0.000938895,0.0002366797,0.0009466386,0.0002693001,0.0002794621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006362403,0.0001293323,0.00209193,0.000447703,0.00007245421,0.0001381359,0.0002635221,0.004318131,0.00001620247,0.9871397,0.00334517,0.001974114],"study_design_scores_gemma":[0.0001358362,0.00005862388,0.0006926368,0.0002946134,0.00008506506,0.000001111858,0.00002747569,0.226162,0.00004888915,0.7717251,0.0004017098,0.0003669182],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1761795,0.000004425279,0.815745,0.00004277713,0.0006622534,0.0002705303,0.0001000621,0.0001559329,0.006839476],"genre_scores_gemma":[0.5534738,0.000002864927,0.4451667,0.0000700215,0.0002092942,9.776053e-7,0.00000534651,0.00002467927,0.00104626],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3772943,"threshold_uncertainty_score":0.9999051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2487698498265217,"score_gpt":0.3022221924911639,"score_spread":0.05345234266464227,"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."}}