{"id":"W4400800651","doi":"10.1093/mnras/stae1700","title":"A statistical framework for recovering intensity mapping autocorrelations from cross-correlations","year":2024,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University; Canadian Institute for Advanced Research","keywords":"Physics; Intensity mapping; Intensity (physics); Statistical analysis; Statistical physics; Statistics; Astrophysics; Optics; Galaxy","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":[0.008814695,0.001045405,0.0008410665,0.003207349,0.0005679739,0.001655065,0.002081387,0.001331212,0.001865657],"category_scores_gemma":[0.02182696,0.0009272569,0.001197434,0.002140302,0.002257622,0.00231787,0.00220274,0.001711759,0.0004550834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000857834,"about_ca_system_score_gemma":0.001516209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006629543,"about_ca_topic_score_gemma":0.004921126,"domain_scores_codex":[0.9978455,0.001001002,0.0001410499,0.0004410695,0.000421297,0.0001500162],"domain_scores_gemma":[0.9878684,0.008468999,0.001246446,0.001009477,0.001168723,0.0002380082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009278121,0.00009077656,0.005799561,0.0001867972,0.0002383022,0.0003348673,0.0001977762,0.692194,0.007667195,0.2008046,0.001161087,0.0912322],"study_design_scores_gemma":[0.000006188918,0.00002376302,0.001100981,0.00001718932,0.00001665986,0.00005230031,0.00001685349,0.9572262,0.0007459738,0.04006723,0.00069919,0.00002738045],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007769529,0.0001295621,0.9914751,0.00009079813,0.00001004889,0.00001249271,0.00007691137,0.0001103786,0.0003251919],"genre_scores_gemma":[0.4294622,0.001015981,0.5645695,0.0002329878,0.0003119947,0.0002086141,0.001002675,0.000300074,0.002896019],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008814695,"threshold_uncertainty_score":0.04661709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013042409766701,"score_gpt":0.2481777847706457,"score_spread":0.2351353750039447,"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."}}